When a consumer (subscription) is registered, messages will be delivered (pushed) by RabbitMQ using the basic.deliver method. The method carries a delivery tag, which uniquely identifies the delivery on a channel.

From Rain RFID Tag IC point of view it is therefore of highest importance to ensure correctness of these data.

Entrepreneurship Defined

four-component instructional design (4C/ID)

Top 10 Important Online Marketing Tools for Entrepreneurs 4.0 Thank you for your rating! Why not leave a message? Any additional feedback or questions are welcome below. Your Name Your Email Address Phone Number Message the Author Cancel By Shiv | Contact Author less than a minute ago Marketing tools are like a helping hand for many entrepreneurs. As time is money using online marketing tools make your job easy and fast and also saves money. Marketing tools are like a helping hand that makes your job so easy. These tools are designed in such a way that it saves you time, effort and money. Apart from finding customers, one of the most difficult problems faced by entrepreneurs is to save time and manage cash flow. Using tools for marketing your business ensures you meet your goal on time. Here are the top 10 most important online marketing tools for entrepreneurs- Hubspot- Inbound Marketing Tool If you want to buy one tool to grow your business exponentially, this is the tool you should buy. Hubspot offers an inbound marketing tool that promises you with attracting leads, converting them and closing the deal. This tool acts as a funnel for your business that narrows downs your audience into your customers and finally generates revenue out of them. You can get started using the Hubspot tool with just $113 per month. You can also get online tutorials on how to use Hubspot tools online or you can always call or email their support team to help you out. Freelancing Graphic Design Platform by EveryDesigns Marketing is impossible without good designs. In marketing, we have a lot of promotional content that uses heavy design work to attract customers. We, humans, are visual animals and it is easier to influence us visually rather than any other means. You can use the EveryDesigns platform to get designs for your business. It is a one-stop destination for your business to get any graphic design services. The innovation that they offer in their services is by organizing a contest for your designs where multiple designers work from around the world. You choose the best design submitted by designers and pay for only that design. You get multiple design options to choose from, along with a 100% money-back guarantee. MailChimp- Email Marketing MailChimp is the best tool when it comes to email marketing. As an entrepreneur, you know how important email marketing is. If you strategize your email marketing outreach, you can attract very valuable customers to your business website. The best part is that it provides some of its features for free, which is enough for a startup or small business to get started and if you want to buy the paid version, then it is as low as $10 per month. Zoom- Conference Meeting Tool Zoom is a web as well as an app-based conference meeting organization tool that helps you in connecting with different team members throughout the world. You can also use this tool to organize a webinar and post it on youtube by recording it. You can pretty much do anything, it is a better version of skype where you can whiteboard as well. The basic version can be used for free and the paid version with pro features starets at $20 for each member. Klusster- Publication Community Kluster is a tool used by digital marketers to spread content and attract customers to their website. Kluster offers a platform to publish content written by you and promote business. As an entrepreneur, you should understand the importance of the backlink for your website. Klusster is one such platform providing quality publications for businesses. The best part it's free. Grammarly-Content Optimization Tool As a marketer or entrepreneur, you know the power of content marketing. It is the most powerful way of branding your business. Creating content can be tough but, the most important thing is creating grammatically correct content. That’s where Grammarly comes and saves the day. It automatically suggests your corrections for every mistake you made. The basic version is free and the paid version with advanced features is $30. You can really see the difference in your writing after using Grammarly. Zoho Social-Social Media Marketing Tool Social media is a platform that has numerous potential and if you are not using social media then you are missing out on a huge opportunity. There are around 4.3 billion social media users and the primary rule of business is where there are people, there is business. Zoho Social is a social media management tool that helps you in promoting your business on multiple social media channels. You can use different channels like Facebook, Twitter Instagram, etc. and post your content, images using one app. UberSuggest- Digital Marketing Tool UberSuggest is a free digital marketing tool provided by Neil Patel Digital. This tool is best for a digital marketer who does not want to spend a big amount of money on buying digital marketing tools. This tool is very easy to use and offers many features that are enough for you to get started with your website’s SEO. Yoast SEO- WordPress Plugin for SEO Yoast SEO is a plugin that you can install on your WordPress site. This tool is a well-researched tool that recommends you SEO corrections that you should make in order to rank your website for specific keywords. It is the best on-page SEO management tool and comes with a one-time price tag of $89 for one website. You can also subscribe to their service starting from $19 per month where you get all the premium features with multiple plugins in one. If you do not want to buy Yoast SEO Plugin then you can also use the free version of the plugin but it is recommended that you should use the premium version if you want to stay ahead off of your competitors. Google- Search Engine & Other Tools You heard it right. Google is a tool that everyone uses and earlier in this post, I’ve said where there are people there is business, therefore, you should also use it. Google's market share when it comes to search engines is 80-90%. As an entrepreneur, you should aim for ranking your website higher in the high volume search keywords. To do that, you would need your website to be optimized according to the Google guidelines and recommendations. Google Offers multiple tools like search console, analytics, tag manager, etc to manage and optimize your website. You should use these tools to make your website better. Conclusion You can use these tools to have leverage over your competitors and quickly increase sales for your business. You are an entrepreneur and time is money for you, therefore, you should use these tools to save time and money. Comment below, what tool are you using to manage your business along with pricing and if you are using any of the above-mentioned tools, tell us your experience. Tags marketing tools / List of best marketing tools

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dieses Mal führte uns Google Maps über einen anderen Weg. Zu Beginn bemerkten wir es gar nicht, aber hier und dort fuhren wir plötzlich Landstraße, wo wir bisher immer auf die Autobahn gelenkt worden waren. Da es keine größeren Umwege waren, dachte ich mir nichts dabei und folgte stur den Anweisungen des Gerätes. Kurz vor dem Ziel wollten wir noch mal ein Stück Autobahn fahren, wichen von der vorgeschlagenen Route ab und steckten plötzlich fest – eine offenbar eben erst errichtete Straßenblockade einiger Bauern versperrte uns den Weg. Google Maps hielt kurz inne und schlug schnell eine Umfahrung vor. Dann hörte ich im Radio, dass französische Bauern schon den ganzen Tag über landesweit für höhere Lebensmittelpreise streikten, viele Auffahrten und Kreuzungen seien von den wütenden Bauern blockiert worden. Da erst wurde mir klar, dass Google uns den ganzen Tag um die Blockaden herumgelenkt hatte

In this volume in the MIT Press Essential Knowledge series, Richard Cytowic, the expert who returned synesthesia to mainstream science after decades of oblivion, offers a concise, accessible primer on this fascinating human experience.

uld argue the text is most guilty of exoticism as it reflects a sense of wonder about different groups of people without any reference to respective racial superiority or inferi

“the Anglo-Saxon readers and viewers of these texts probably considered them true”

CRM means greater efficiency for multiple teams. Automatically stored communication allows you to view emails, calendar, and phone call details in one easily accessible place. Add that to the ability for multiple teams to access the same information, and the amount of achievable progress simply skyrockets. Sales, marketing, and customer service teams can share valuable information about clients to continue to funnel them down the pipeline to get the desired result of closing a sale, knowledge of new products, or excellent customer service. Every department can now tag team to get the right information to the right individual. With this new found ease, teams can seamlessly work together to improve the bottom line.

m Bereich der Theorie.

For whatever be the knowledge which we are able to obtain of God, either by perception or reflection, we must of necessity believe that He is by many degrees far better than what we perceive Him to be.

It’s worth noting that first line of the script starts with #!. It is a special directive which Unix treats differently.

spielerischer Überwachung keineswegs nur in Fernost, sondern auch im Westen angewandt werden, treffen Berichte über das chinesische SCS hierzulande meist auf großes Befremden.

Equivalency Theory

Theory of Interaction and Communication

Theory of Industrialization of Teaching

[mp4 @ 0x7fe98400dc00] track 1: could not find tag, codec not currently supported in container This means that pcm_mulaw is not allowed in the MP4 output container format. Your choices are: Use a different output container format ffmpeg -i input -codec copy output.mkv Or re-encode the audio ffmpeg -i input -c:v copy -c:a aac output.mp4

Encode the audio to a supported format, such as AAC by replacing -acodec copy with -c:a aac, or use a more flexible output container format that does support pcm_alaw, such as MOV or MKV.

it's an embarrassment to Wikipedia to have the AfD tag on top

p-value=PH0[|¯¯¯¯Y−μY,0|>|¯¯¯¯Yact−μY,0|](3.2)(3.2)p-value=PH0[|Y¯−μY,0|>|Y¯act−μY,0|]\begin{equation} p \text{-value} = P_{H_0}\left[ \lvert \overline{Y} - \mu_{Y,0} \rvert > \lvert \overline{Y}^{act} - \mu_{Y,0} \rvert \right] \tag{3.2} \end{equation} In (3.2), ¯¯¯¯YactY¯act\overline{Y}^{act} is the mean of the sample actually computed. Consequently, in order to compute the ppp-value as in (3.2), knowledge about the sampling distribution of ¯¯¯¯YY¯\overline{Y} when the null hypothesis is true is required. However in most cases the sampling distribution of ¯¯¯¯YY¯\overline{Y} is unknown. Fortunately, as stated by the CLT (see Key Concept 2.7), the large-sample approximation ¯¯¯¯Y≈N(μY,0,σ2¯¯¯¯Y)  ,  σ2¯¯¯¯Y=σ2YnY¯≈N(μY,0,σY¯2)  ,  σY¯2=σY2n \overline{Y} \approx \mathcal{N}(\mu_{Y,0}, \, \sigma^2_{\overline{Y}}) \ \ , \ \ \sigma^2_{\overline{Y}} = \frac{\sigma_Y^2}{n} can be made under the null. Thus, ¯¯¯¯Y−μY,0σY/√n∼N(0,1).Y¯−μY,0σY/n∼N(0,1). \frac{\overline{Y} - \mu_{Y,0}}{\sigma_Y/\sqrt{n}} \sim \mathcal{N}(0,1). So in large samples, the ppp-value can be computed without knowledge of the exact sampling distribution of ¯¯¯¯YY¯\overline{Y}. Calculating the p-Value when the Standard Deviation is Known For now, let us assume that σ¯¯¯¯YσY¯\sigma_{\overline{Y}} is known. Then, we can rewrite (3.2) as p-value=PH0⎡⎢⎣∣∣ ∣∣¯¯¯¯Y−μY,0σ¯¯¯¯Y∣∣ ∣∣>∣∣ ∣∣¯¯¯¯Yact−μY,0σ¯¯¯¯Y∣∣ ∣∣⎤⎥⎦=2⋅Φ⎡⎢⎣−∣∣ ∣∣¯¯¯¯Yact−μY,0σ¯¯¯¯Y∣∣ ∣∣⎤⎥⎦.(3.3)p-value=PH0[|Y¯−μY,0σY¯|>|Y¯act−μY,0σY¯|](3.3)=2⋅Φ[−|Y¯act−μY,0σY¯|].\begin{align} p \text{-value} =& \, P_{H_0}\left[ \left\lvert \frac{\overline{Y} - \mu_{Y,0}}{\sigma_{\overline{Y}}} \right\rvert > \left\lvert \frac{\overline{Y}^{act} - \mu_{Y,0}}{\sigma_{\overline{Y}}} \right\rvert \right] \\ =& \, 2 \cdot \Phi \left[ - \left\lvert \frac{\overline{Y}^{act} - \mu_{Y,0}}{\sigma_{\overline{Y}}} \right\rvert\right]. \tag{3.3} \end{align} The ppp-value can be seen as the area in the tails of the N(0,1)N(0,1)\mathcal{N}(0,1) distribution that lies beyond ±∣∣ ∣∣¯¯¯¯Yact−μY,0σ¯¯¯¯Y∣∣ ∣∣

Alternatively, you can use the Explore function to ask Google Sheets any number of questions about your to do list. It'll give you suggestions like "Most frequent Task" or "What percentage of Tag is [tag name]" and so on. This can give you insight into what you're working on the most and how you're spending your time, which can help you plan your workdays more productively.

As neither microautophagy, which involves direct uptake ofbulk cargo by lysosomes [10], nor CMA, which degrades a small subset of proteins via recognitionof a specific protein tag [11], have been implicated in host-pathogen defense against noroviruses,macroautophagy will primarily be discussed here

What is telling is the list of blogs that have refereed traffic to my blog are all written by men. I am not sure what – or if – there is anything to make out of that fact, but anytime I see a list of people that is exclusively male it does make me pause and go hmmmm.

And I am planning on cutting back on my personal use of social media (easier said than done) and want to try to return to using my blog more than Twitter for sharing.

inscription

Classes encapsulate code into instantiable units. Namespaces group functions, classes, and constants into spaces where their name is unique.The namespace declaration must occur straight after the opening <?php tag and no other statements may precede it.Namespaces affect constants, but you must declare them with the const keyword and not with define()

Types of questions and where to ask: How do I? -- ask on Server Fault (tell them what tags to use -- your product tag at minimum) I got this error, why? -- ask on Server Fault I got this error and I'm sure it's a bug -- report it on your own site I have an idea/request -- report it on your own site Why do you? -- ask in your own community (support forum, etc) When will you? -- ask in your own community

Since the time of Henry I, it has been used by the reigning monarch and is the longest-occupied palace in Europe

Archiving and downloading annotations Annotation viewing and export, from Hypothesis Labs Link: https://jonudell.info/h/facet Screencast: https://jonudell.net/h/facet.mp4 Description:  View annotations by user, group, URL, or tag. Export results to HTML, CSV, text, or Markdown.

Leo: Stephen in Glasgow, Scotland shares his recent NFC experience: I think I know of a problem with NFC. When I first got my Galaxy S3 it would quite often beep for no apparent reason. Every time I put it in my jacket pocket or on my table, it would beep. Then I noticed it was when I put it on my table resting on my wallet that it was beeping. I felt like an idiot for not figuring it out. Some of my newer credit cards have RFID chips inside for the new contactless payment systems. We don't have these in the U.S. yet; or, if we do, they're in very limited areas.Steve: Yes.Leo: One of the problems I had, when you go to Europe it's hard to buy gas because our credit cards are dumb, and you need a smart credit card to buy gas. And the Galaxy S3's reader was shouting out, "Hey, I found a tag." And sure enough, when I downloaded an NFC app from the Android store, the beep would then be accompanied by the card info displayed on the screen when I put my S3 near my wallet. If these phones are going to go crazy when we put them near a wallet with RFID cards, no wonder Apple is holding back. As far as I can see, there is no way to tell Android to ignore a tag. And even if you could, would that use battery as the RFID tag in your wallet was constantly shouting out, "Hey, hey, I'm here," and your phone listened to the details before ignoring it again? Love the show. That's a great question.

The url also contains an optional refspec (#v0.0.1) that tells npm which branch, commit, or in this case tag you want to have checked out.

We passed Milestone A!

Alexander Samuel reflects on tagging and its origins as a backbone to the social web. Along with RSS, tags allowed users to connect and collate content using such tools as feed readers. This all changed with the advent of social media and the algorithmically curated news feed.

function flow(bytes32 ilk) external note { require(debt != 0, "End/debt-zero"); require(fix[ilk] == 0, "End/fix-ilk-already-defined"); (, uint rate,,,) = vat.ilks(ilk); uint256 wad = rmul(rmul(Art[ilk], rate), tag[ilk]); fix[ilk] = rdiv(mul(sub(wad, gap[ilk]), RAY), debt); }

function skim(bytes32 ilk, address urn) external note { require(tag[ilk] != 0, "End/tag-ilk-not-defined"); (, uint rate,,,) = vat.ilks(ilk); (uint ink, uint art) = vat.urns(ilk, urn); uint owe = rmul(rmul(art, rate), tag[ilk]); uint wad = min(ink, owe); gap[ilk] = add(gap[ilk], sub(owe, wad)); require(wad <= 2**255 && art <= 2**255, "End/overflow"); vat.grab(ilk, urn, address(this), address(vow), -int(wad), -int(art)); }

function skip(bytes32 ilk, uint256 id) external note { require(tag[ilk] != 0, "End/tag-ilk-not-defined"); (address flipV,,) = cat.ilks(ilk); Flippy flip = Flippy(flipV); (, uint rate,,,) = vat.ilks(ilk); (uint bid, uint lot,,,, address usr,, uint tab) = flip.bids(id); vat.suck(address(vow), address(vow), tab); vat.suck(address(vow), address(this), bid); vat.hope(address(flip)); flip.yank(id); uint art = tab / rate; Art[ilk] = add(Art[ilk], art); require(int(lot) >= 0 && int(art) >= 0, "End/overflow"); vat.grab(ilk, usr, address(this), address(vow), int(lot), int(art)); }

A UMD build will let people use your module via a global variable by dropping it into a <script> tag - this makes it easier to try without any build tooling in an HTML file, and in tools like JS Bin and CodePen.

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c.

The narrative is a hypertext story that changes every time the reader interacts with it, to create the sense of an unreliable narrator and stand as an allegory for the process of memory.

Jacqueline knew better than to interrupt her sister when she was this heated; you don’t get to be the richest woman in the tech industry without a ruthless reputation. 

Jacqueline fired back at her sister.

Lizzie said, still shouting.

Zuhause

Anything inside the <FancyBorder> JSX tag gets passed into the FancyBorder component as a children prop. Since FancyBorder renders {props.children} inside a <div>, the passed elements appear in the final output.

America the safe haven and America the self-absorbed, even the self-righteous America that assumes the right to impose enlightenment on foreign nations

a man is rich in proportion to the number of things which he can afford to let alone

comment trouver les principes ?

decoding and deterritorialization

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Domain

Example

result.

uint owe = rmul(rmul(u.art, i.rate), tag[ilk]);

tag[ilk] = wdiv(spot.par(), uint(spot.ilks(ilk).pip.read()));

mapping (bytes32 => uint256) public tag; // cage price [ray]

type FindByTag<Union, Tag> = Union extends { tag: Tag } ? Union : never; function cast<A extends Foo["tag"]>(foo: Foo, expectedTag: A): FindByTag<Foo, A> { if (foo.tag !== expectedTag) throw Error(`expected tag ${expectedTag} but was ${foo.tag}`) return foo as FindByTag<Foo, A>; }

Another is entitled “The Great Fight.” It was between Tom Hyer and Yankee Sullivan, both bare-knuckled, at Still Pond Heights, Maryland, in 1849. It was won by Hyer in sixteen rounds, and the prize was $10,000. The judges wore top hats. The brass title tag on another engraving reads, “Rescue of Colonel Thomas J. Kelly and Captain Timothy Deacy by Members of the Irish Revolutionary Brotherhood from the English Government at Manchester, England, September 18, 1867.” A copy of the Emancipation Proclamation is on this wall; so, almost inevitably, is a facsimile of Lincoln’s saloon licence. An engraving of Washington and his generals hangs next to an engraving of a session of the Great Parliament of Ireland.

A conexão e colaboração entre os associados é um valor levado a sério pelos coworkings. E nada melhor do que criar e manter uma rede de relacionamento bem estruturada, de acordo com os seus objetivos. E como esses espaços prezam pelo compartilhamento e trabalho co-criativo,

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an anxious teacher isn’t the most convincing, so I try to swallow my nerves and to smile confidently at them as they enter the room.

Letters of transit signed by General de Gaulle. Cannot be rescinded, not even questioned.Rick appears ready to take them form Ugarte.

As a prototype it hits a sweet spot: it's challenging - it's no small feat to recognize handwritten digits - but it's not so difficult as to require an extremely complicated solution, or tremendous computational power. Furthermore, it's a great way to develop more advanced techniques, such as deep learning. And so throughout the book we'll return repeatedly to the problem of handwriting recognition. Later in the book, we'll discuss how these ideas may be applied to other problems in computer vision, and also in speech, natural language processing, and other domains.Of course, if the point of the chapter was only to write a computer program to recognize handwritten digits, then the chapter would be much shorter! But along the way we'll develop many key ideas about neural networks, including two important types of artificial neuron (the perceptron and the sigmoid neuron), and the standard learning algorithm for neural networks, known as stochastic gradient descent. Throughout, I focus on explaining why things are done the way they are, and on building your neural networks intuition. That requires a lengthier discussion than if I just presented the basic mechanics of what's going on, but it's worth it for the deeper understanding you'll attain. Amongst the payoffs, by the end of the chapter we'll be in position to understand what deep learning is, and why it matters.PerceptronsWhat is a neural network? To get started, I'll explain a type of artificial neuron called a perceptron. Perceptrons were developed in the 1950s and 1960s by the scientist Frank Rosenblatt, inspired by earlier work by Warren McCulloch and Walter Pitts. Today, it's more common to use other models of artificial neurons - in this book, and in much modern work on neural networks, the main neuron model used is one called the sigmoid neuron. We'll get to sigmoid neurons shortly. But to understand why sigmoid neurons are defined the way they are, it's worth taking the time to first understand perceptrons.So how do perceptrons work? A perceptron takes several binary inputs, x1,x2,…x1,x2,…x_1, x_2, \ldots, and produces a single binary output: In the example shown the perceptron has three inputs, x1,x2,x3x1,x2,x3x_1, x_2, x_3. In general it could have more or fewer inputs. Rosenblatt proposed a simple rule to compute the output. He introduced weights, w1,w2,…w1,w2,…w_1,w_2,\ldots, real numbers expressing the importance of the respective inputs to the output. The neuron's output, 000 or 111, is determined by whether the weighted sum ∑jwjxj∑jwjxj\sum_j w_j x_j is less than or greater than some threshold value. Just like the weights, the threshold is a real number which is a parameter of the neuron. To put it in more precise algebraic terms: output={01if ∑jwjxj≤ thresholdif ∑jwjxj> threshold(1)(1)output={0if ∑jwjxj≤ threshold1if ∑jwjxj> threshold\begin{eqnarray} \mbox{output} & = & \left\{ \begin{array}{ll} 0 & \mbox{if } \sum_j w_j x_j \leq \mbox{ threshold} \\ 1 & \mbox{if } \sum_j w_j x_j > \mbox{ threshold} \end{array} \right. \tag{1}\end{eqnarray} That's all there is to how a perceptron works!That's the basic mathematical model. A way you can think about the perceptron is that it's a device that makes decisions by weighing up evidence. Let me give an example. It's not a very realistic example, but it's easy to understand, and we'll soon get to more realistic examples. Suppose the weekend is coming up, and you've heard that there's going to be a cheese festival in your city. You like cheese, and are trying to decide whether or not to go to the festival. You might make your decision by weighing up three factors: Is the weather good? Does your boyfriend or girlfriend want to accompany you? Is the festival near public transit? (You don't own a car). We can represent these three factors by corresponding binary variables x1,x2x1,x2x_1, x_2, and x3x3x_3. For instance, we'd have x1=1x1=1x_1 = 1 if the weather is good, and x1=0x1=0x_1 = 0 if the weather is bad. Similarly, x2=1x2=1x_2 = 1 if your boyfriend or girlfriend wants to go, and x2=0x2=0x_2 = 0 if not. And similarly again for x3x3x_3 and public transit.Now, suppose you absolutely adore cheese, so much so that you're happy to go to the festival even if your boyfriend or girlfriend is uninterested and the festival is hard to get to. But perhaps you really loathe bad weather, and there's no way you'd go to the festival if the weather is bad. You can use perceptrons to model this kind of decision-making. One way to do this is to choose a weight w1=6w1=6w_1 = 6 for the weather, and w2=2w2=2w_2 = 2 and w3=2w3=2w_3 = 2 for the other conditions. The larger value of w1w1w_1 indicates that the weather matters a lot to you, much more than whether your boyfriend or girlfriend joins you, or the nearness of public transit. Finally, suppose you choose a threshold of 555 for the perceptron. With these choices, the perceptron implements the desired decision-making model, outputting 111 whenever the weather is good, and 000 whenever the weather is bad. It makes no difference to the output whether your boyfriend or girlfriend wants to go, or whether public transit is nearby.By varying the weights and the threshold, we can get different models of decision-making. For example, suppose we instead chose a threshold of 333. Then the perceptron would decide that you should go to the festival whenever the weather was good or when both the festival was near public transit and your boyfriend or girlfriend was willing to join you. In other words, it'd be a different model of decision-making. Dropping the threshold means you're more willing to go to the festival.Obviously, the perceptron isn't a complete model of human decision-making! But what the example illustrates is how a perceptron can weigh up different kinds of evidence in order to make decisions. And it should seem plausible that a complex network of perceptrons could make quite subtle decisions: In this network, the first column of perceptrons - what we'll call the first layer of perceptrons - is making three very simple decisions, by weighing the input evidence. What about the perceptrons in the second layer? Each of those perceptrons is making a decision by weighing up the results from the first layer of decision-making. In this way a perceptron in the second layer can make a decision at a more complex and more abstract level than perceptrons in the first layer. And even more complex decisions can be made by the perceptron in the third layer. In this way, a many-layer network of perceptrons can engage in sophisticated decision making.Incidentally, when I defined perceptrons I said that a perceptron has just a single output. In the network above the perceptrons look like they have multiple outputs. In fact, they're still single output. The multiple output arrows are merely a useful way of indicating that the output from a perceptron is being used as the input to several other perceptrons. It's less unwieldy than drawing a single output line which then splits.Let's simplify the way we describe perceptrons. The condition ∑jwjxj>threshold∑jwjxj>threshold\sum_j w_j x_j > \mbox{threshold} is cumbersome, and we can make two notational changes to simplify it. The first change is to write ∑jwjxj∑jwjxj\sum_j w_j x_j as a dot product, w⋅x≡∑jwjxjw⋅x≡∑jwjxjw \cdot x \equiv \sum_j w_j x_j, where www and xxx are vectors whose components are the weights and inputs, respectively. The second change is to move the threshold to the other side of the inequality, and to replace it by what's known as the perceptron's bias, b≡−thresholdb≡−thresholdb \equiv -\mbox{threshold}. Using the bias instead of the threshold, the perceptron rule can be rewritten: output={01if w⋅x+b≤0if w⋅x+b>0(2)(2)output={0if w⋅x+b≤01if w⋅x+b>0\begin{eqnarray} \mbox{output} = \left\{ \begin{array}{ll} 0 & \mbox{if } w\cdot x + b \leq 0 \\ 1 & \mbox{if } w\cdot x + b > 0 \end{array} \right. \tag{2}\end{eqnarray} You can think of the bias as a measure of how easy it is to get the perceptron to output a 111. Or to put it in more biological terms, the bias is a measure of how easy it is to get the perceptron to fire. For a perceptron with a really big bias, it's extremely easy for the perceptron to output a 111. But if the bias is very negative, then it's difficult for the perceptron to output a 111. Obviously, introducing the bias is only a small change in how we describe perceptrons, but we'll see later that it leads to further notational simplifications. Because of this, in the remainder of the book we won't use the threshold, we'll always use the bias.I've described perceptrons as a method for weighing evidence to make decisions. Another way perceptrons can be used is to compute the elementary logical functions we usually think of as underlying computation, functions such as AND, OR, and NAND. For example, suppose we have a perceptron with two inputs, each with weight −2−2-2, and an overall bias of 333. Here's our perceptron: Then we see that input 000000 produces output 111, since (−2)∗0+(−2)∗0+3=3(−2)∗0+(−2)∗0+3=3(-2)*0+(-2)*0+3 = 3 is positive. Here, I've introduced the ∗∗* symbol to make the multiplications explicit. Similar calculations show that the inputs 010101 and 101010 produce output 111. But the input 111111 produces output 000, since (−2)∗1+(−2)∗1+3=−1(−2)∗1+(−2)∗1+3=−1(-2)*1+(-2)*1+3 = -1 is negative. And so our perceptron implements a NAND gate!The NAND example shows that we can use perceptrons to compute simple logical functions. In fact, we can use networks of perceptrons to compute any logical function at all. The reason is that the NAND gate is universal for computation, that is, we can build any computation up out of NAND gates. For example, we can use NAND gates to build a circuit which adds two bits, x1x1x_1 and x2x2x_2. This requires computing the bitwise sum, x1⊕x2x1⊕x2x_1 \oplus x_2, as well as a carry bit which is set to 111 when both x1x1x_1 and x2x2x_2 are 111, i.e., the carry bit is just the bitwise product x1x2x1x2x_1 x_2: To get an equivalent network of perceptrons we replace all the NAND gates by perceptrons with two inputs, each with weight −2−2-2, and an overall bias of 333. Here's the resulting network. Note that I've moved the perceptron corresponding to the bottom right NAND gate a little, just to make it easier to draw the arrows on the diagram: One notable aspect of this network of perceptrons is that the output from the leftmost perceptron is used twice as input to the bottommost perceptron. When I defined the perceptron model I didn't say whether this kind of double-output-to-the-same-place was allowed. Actually, it doesn't much matter. If we don't want to allow this kind of thing, then it's possible to simply merge the two lines, into a single connection with a weight of -4 instead of two connections with -2 weights. (If you don't find this obvious, you should stop and prove to yourself that this is equivalent.) With that change, the network looks as follows, with all unmarked weights equal to -2, all biases equal to 3, and a single weight of -4, as marked: Up to now I've been drawing inputs like x1x1x_1 and x2x2x_2 as variables floating to the left of the network of perceptrons. In fact, it's conventional to draw an extra layer of perceptrons - the input layer - to encode the inputs: This notation for input perceptrons, in which we have an output, but no inputs, is a shorthand. It doesn't actually mean a perceptron with no inputs. To see this, suppose we did have a perceptron with no inputs. Then the weighted sum ∑jwjxj∑jwjxj\sum_j w_j x_j would always be zero, and so the perceptron would output 111 if b>0b>0b > 0, and 000 if b≤0b≤0b \leq 0. That is, the perceptron would simply output a fixed value, not the desired value (x1x1x_1, in the example above). It's better to think of the input perceptrons as not really being perceptrons at all, but rather special units which are simply defined to output the desired values, x1,x2,…x1,x2,…x_1, x_2,\ldots.The adder example demonstrates how a network of perceptrons can be used to simulate a circuit containing many NAND gates. And because NAND gates are universal for computation, it follows that perceptrons are also universal for computation.The computational universality of perceptrons is simultaneously reassuring and disappointing. It's reassuring because it tells us that networks of perceptrons can be as powerful as any other computing device. But it's also disappointing, because it makes it seem as though perceptrons are merely a new type of NAND gate. That's hardly big news!However, the situation is better than this view suggests. It turns out that we can devise learning algorithms which can automatically tune the weights and biases of a network of artificial neurons. This tuning happens in response to external stimuli, without direct intervention by a programmer. These learning algorithms enable us to use artificial neurons in a way which is radically different to conventional logic gates. Instead of explicitly laying out a circuit of NAND and other gates, our neural networks can simply learn to solve problems, sometimes problems where it would be extremely difficult to directly design a conventional circuit.Sigmoid neuronsLearning algorithms sound terrific. But how can we devise such algorithms for a neural network? Suppose we have a network of perceptrons that we'd like to use to learn to solve some problem. For example, the inputs to the network might be the raw pixel data from a scanned, handwritten image of a digit. And we'd like the network to learn weights and biases so that the output from the network correctly classifies the digit. To see how learning might work, suppose we make a small change in some weight (or bias) in the network. What we'd like is for this small change in weight to cause only a small corresponding change in the output from the network. As we'll see in a moment, this property will make learning possible. Schematically, here's what we want (obviously this network is too simple to do handwriting recognition!): If it were true that a small change in a weight (or bias) causes only a small change in output, then we could use this fact to modify the weights and biases to get our network to behave more in the manner we want. For example, suppose the network was mistakenly classifying an image as an "8" when it should be a "9". We could figure out how to make a small change in the weights and biases so the network gets a little closer to classifying the image as a "9". And then we'd repeat this, changing the weights and biases over and over to produce better and better output. The network would be learning.The problem is that this isn't what happens when our network contains perceptrons. In fact, a small change in the weights or bias of any single perceptron in the network can sometimes cause the output of that perceptron to completely flip, say from 000 to 111. That flip may then cause the behaviour of the rest of the network to completely change in some very complicated way. So while your "9" might now be classified correctly, the behaviour of the network on all the other images is likely to have completely changed in some hard-to-control way. That makes it difficult to see how to gradually modify the weights and biases so that the network gets closer to the desired behaviour. Perhaps there's some clever way of getting around this problem. But it's not immediately obvious how we can get a network of perceptrons to learn.We can overcome this problem by introducing a new type of artificial neuron called a sigmoid neuron. Sigmoid neurons are similar to perceptrons, but modified so that small changes in their weights and bias cause only a small change in their output. That's the crucial fact which will allow a network of sigmoid neurons to learn.Okay, let me describe the sigmoid neuron. We'll depict sigmoid neurons in the same way we depicted perceptrons: Just like a perceptron, the sigmoid neuron has inputs, x1,x2,…x1,x2,…x_1, x_2, \ldots. But instead of being just 000 or 111, these inputs can also take on any values between 000 and 111. So, for instance, 0.638…0.638…0.638\ldots is a valid input for a sigmoid neuron. Also just like a perceptron, the sigmoid neuron has weights for each input, w1,w2,…w1,w2,…w_1, w_2, \ldots, and an overall bias, bbb. But the output is not 000 or 111. Instead, it's σ(w⋅x+b)σ(w⋅x+b)\sigma(w \cdot x+b), where σσ\sigma is called the sigmoid function* *Incidentally, σσ\sigma is sometimes called the logistic function, and this new class of neurons called logistic neurons. It's useful to remember this terminology, since these terms are used by many people working with neural nets. However, we'll stick with the sigmoid terminology., and is defined by: σ(z)≡11+e−z.(3)(3)σ(z)≡11+e−z.\begin{eqnarray} \sigma(z) \equiv \frac{1}{1+e^{-z}}. \tag{3}\end{eqnarray} To put it all a little more explicitly, the output of a sigmoid neuron with inputs x1,x2,…x1,x2,…x_1,x_2,\ldots, weights w1,w2,…w1,w2,…w_1,w_2,\ldots, and bias bbb is 11+exp(−∑jwjxj−b).(4)(4)11+exp⁡(−∑jwjxj−b).\begin{eqnarray} \frac{1}{1+\exp(-\sum_j w_j x_j-b)}. \tag{4}\end{eqnarray}At first sight, sigmoid neurons appear very different to perceptrons. The algebraic form of the sigmoid function may seem opaque and forbidding if you're not already familiar with it. In fact, there are many similarities between perceptrons and sigmoid neurons, and the algebraic form of the sigmoid function turns out to be more of a technical detail than a true barrier to understanding.To understand the similarity to the perceptron model, suppose z≡w⋅x+bz≡w⋅x+bz \equiv w \cdot x + b is a large positive number. Then e−z≈0e−z≈0e^{-z} \approx 0 and so σ(z)≈1σ(z)≈1\sigma(z) \approx 1. In other words, when z=w⋅x+bz=w⋅x+bz = w \cdot x+b is large and positive, the output from the sigmoid neuron is approximately 111, just as it would have been for a perceptron. Suppose on the other hand that z=w⋅x+bz=w⋅x+bz = w \cdot x+b is very negative. Then e−z→∞e−z→∞e^{-z} \rightarrow \infty, and σ(z)≈0σ(z)≈0\sigma(z) \approx 0. So when z=w⋅x+bz=w⋅x+bz = w \cdot x +b is very negative, the behaviour of a sigmoid neuron also closely approximates a perceptron. It's only when w⋅x+bw⋅x+bw \cdot x+b is of modest size that there's much deviation from the perceptron model.What about the algebraic form of σσ\sigma? How can we understand that? In fact, the exact form of σσ\sigma isn't so important - what really matters is the shape of the function when plotted. Here's the shape: -4-3-2-1012340.00.20.40.60.81.0zsigmoid function function s(x) {return 1/(1+Math.exp(-x));} var m = [40, 120, 50, 120]; var height = 290 - m[0] - m[2]; var width = 600 - m[1] - m[3]; var xmin = -5; var xmax = 5; var sample = 400; var x1 = d3.scale.linear().domain([0, sample]).range([xmin, xmax]); var data = d3.range(sample).map(function(d){ return { x: x1(d), y: s(x1(d))}; }); var x = d3.scale.linear().domain([xmin, xmax]).range([0, width]); var y = d3.scale.linear() .domain([0, 1]) .range([height, 0]); var line = d3.svg.line() .x(function(d) { return x(d.x); }) .y(function(d) { return y(d.y); }) var graph = d3.select("#sigmoid_graph") .append("svg") .attr("width", width + m[1] + m[3]) .attr("height", height + m[0] + m[2]) .append("g") .attr("transform", "translate(" + m[3] + "," + m[0] + ")"); var xAxis = d3.svg.axis() .scale(x) .tickValues(d3.range(-4, 5, 1)) .orient("bottom") graph.append("g") .attr("class", "x axis") .attr("transform", "translate(0, " + height + ")") .call(xAxis); var yAxis = d3.svg.axis() .scale(y) .tickValues(d3.range(0, 1.01, 0.2)) .orient("left") .ticks(5) graph.append("g") .attr("class", "y axis") .call(yAxis); graph.append("path").attr("d", line(data)); graph.append("text") .attr("class", "x label") .attr("text-anchor", "end") .attr("x", width/2) .attr("y", height+35) .text("z"); graph.append("text") .attr("x", (width / 2)) .attr("y", -10) .attr("text-anchor", "middle") .style("font-size", "16px") .text("sigmoid function"); This shape is a smoothed out version of a step function: -4-3-2-1012340.00.20.40.60.81.0zstep function function s(x) {return x < 0 ? 0 : 1;} var m = [40, 120, 50, 120]; var height = 290 - m[0] - m[2]; var width = 600 - m[1] - m[3]; var xmin = -5; var xmax = 5; var sample = 400; var x1 = d3.scale.linear().domain([0, sample]).range([xmin, xmax]); var data = d3.range(sample).map(function(d){ return { x: x1(d), y: s(x1(d))}; }); var x = d3.scale.linear().domain([xmin, xmax]).range([0, width]); var y = d3.scale.linear() .domain([0,1]) .range([height, 0]); var line = d3.svg.line() .x(function(d) { return x(d.x); }) .y(function(d) { return y(d.y); }) var graph = d3.select("#step_graph") .append("svg") .attr("width", width + m[1] + m[3]) .attr("height", height + m[0] + m[2]) .append("g") .attr("transform", "translate(" + m[3] + "," + m[0] + ")"); var xAxis = d3.svg.axis() .scale(x) .tickValues(d3.range(-4, 5, 1)) .orient("bottom") graph.append("g") .attr("class", "x axis") .attr("transform", "translate(0, " + height + ")") .call(xAxis); var yAxis = d3.svg.axis() .scale(y) .tickValues(d3.range(0, 1.01, 0.2)) .orient("left") .ticks(5) graph.append("g") .attr("class", "y axis") .call(yAxis); graph.append("path").attr("d", line(data)); graph.append("text") .attr("class", "x label") .attr("text-anchor", "end") .attr("x", width/2) .attr("y", height+35) .text("z"); graph.append("text") .attr("x", (width / 2)) .attr("y", -10) .attr("text-anchor", "middle") .style("font-size", "16px") .text("step function"); If σσ\sigma had in fact been a step function, then the sigmoid neuron would be a perceptron, since the output would be 111 or 000 depending on whether w⋅x+bw⋅x+bw\cdot x+b was positive or negative* *Actually, when w⋅x+b=0w⋅x+b=0w \cdot x +b = 0 the perceptron outputs 000, while the step function outputs 111. So, strictly speaking, we'd need to modify the step function at that one point. But you get the idea.. By using the actual σσ\sigma function we get, as already implied above, a smoothed out perceptron. Indeed, it's the smoothness of the σσ\sigma function that is the crucial fact, not its detailed form. The smoothness of σσ\sigma means that small changes ΔwjΔwj\Delta w_j in the weights and ΔbΔb\Delta b in the bias will produce a small change ΔoutputΔoutput\Delta \mbox{output} in the output from the neuron. In fact, calculus tells us that ΔoutputΔoutput\Delta \mbox{output} is well approximated by Δoutput≈∑j∂output∂wjΔwj+∂output∂bΔb,(5)(5)Δoutput≈∑j∂output∂wjΔwj+∂output∂bΔb,\begin{eqnarray} \Delta \mbox{output} \approx \sum_j \frac{\partial \, \mbox{output}}{\partial w_j} \Delta w_j + \frac{\partial \, \mbox{output}}{\partial b} \Delta b, \tag{5}\end{eqnarray} where the sum is over all the weights, wjwjw_j, and ∂output/∂wj∂output/∂wj\partial \, \mbox{output} / \partial w_j and ∂output/∂b∂output/∂b\partial \, \mbox{output} /\partial b denote partial derivatives of the outputoutput\mbox{output} with respect to wjwjw_j and bbb, respectively. Don't panic if you're not comfortable with partial derivatives! While the expression above looks complicated, with all the partial derivatives, it's actually saying something very simple (and which is very good news): ΔoutputΔoutput\Delta \mbox{output} is a linear function of the changes ΔwjΔwj\Delta w_j and ΔbΔb\Delta b in the weights and bias. This linearity makes it easy to choose small changes in the weights and biases to achieve any desired small change in the output. So while sigmoid neurons have much of the same qualitative behaviour as perceptrons, they make it much easier to figure out how changing the weights and biases will change the output.If it's the shape of σσ\sigma which really matters, and not its exact form, then why use the particular form used for σσ\sigma in Equation (3)σ(z)≡11+e−zσ(z)≡11+e−z\begin{eqnarray} \sigma(z) \equiv \frac{1}{1+e^{-z}} \nonumber\end{eqnarray}$('#margin_387419264610_reveal').click(function() {$('#margin_387419264610').toggle('slow', function() {});});? In fact, later in the book we will occasionally consider neurons where the output is f(w⋅x+b)f(w⋅x+b)f(w \cdot x + b) for some other activation function f(⋅)f(⋅)f(\cdot). The main thing that changes when we use a different activation function is that the particular values for the partial derivatives in Equation (5)Δoutput≈∑j∂output∂wjΔwj+∂output∂bΔbΔoutput≈∑j∂output∂wjΔwj+∂output∂bΔb\begin{eqnarray} \Delta \mbox{output} \approx \sum_j \frac{\partial \, \mbox{output}}{\partial w_j} \Delta w_j + \frac{\partial \, \mbox{output}}{\partial b} \Delta b \nonumber\end{eqnarray}$('#margin_727997094331_reveal').click(function() {$('#margin_727997094331').toggle('slow', function() {});}); change. It turns out that when we compute those partial derivatives later, using σσ\sigma will simplify the algebra, simply because exponentials have lovely properties when differentiated. In any case, σσ\sigma is commonly-used in work on neural nets, and is the activation function we'll use most often in this book.How should we interpret the output from a sigmoid neuron? Obviously, one big difference between perceptrons and sigmoid neurons is that sigmoid neurons don't just output 000 or 111. They can have as output any real number between 000 and 111, so values such as 0.173…0.173…0.173\ldots and 0.689…0.689…0.689\ldots are legitimate outputs. This can be useful, for example, if we want to use the output value to represent the average intensity of the pixels in an image input to a neural network. But sometimes it can be a nuisance. Suppose we want the output from the network to indicate either "the input image is a 9" or "the input image is not a 9". Obviously, it'd be easiest to do this if the output was a 000 or a 111, as in a perceptron. But in practice we can set up a convention to deal with this, for example, by deciding to interpret any output of at least 0.50.50.5 as indicating a "9", and any output less than 0.50.50.5 as indicating "not a 9". I'll always explicitly state when we're using such a convention, so it shouldn't cause any confusion. Exercises Sigmoid neurons simulating perceptrons, part I \mbox{} Suppose we take all the weights and biases in a network of perceptrons, and multiply them by a positive constant, c>0c>0c > 0. Show that the behaviour of the network doesn't change.Sigmoid neurons simulating perceptrons, part II \mbox{} Suppose we have the same setup as the last problem - a network of perceptrons. Suppose also that the overall input to the network of perceptrons has been chosen. We won't need the actual input value, we just need the input to have been fixed. Suppose the weights and biases are such that w⋅x+b≠0w⋅x+b≠0w \cdot x + b \neq 0 for the input xxx to any particular perceptron in the network. Now replace all the perceptrons in the network by sigmoid neurons, and multiply the weights and biases by a positive constant c>0c>0c > 0. Show that in the limit as c→∞c→∞c \rightarrow \infty the behaviour of this network of sigmoid neurons is exactly the same as the network of perceptrons. How can this fail when w⋅x+b=0w⋅x+b=0w \cdot x + b = 0 for one of the perceptrons? The architecture of neural networksIn the next section I'll introduce a neural network that can do a pretty good job classifying handwritten digits. In preparation for that, it helps to explain some terminology that lets us name different parts of a network. Suppose we have the network: As mentioned earlier, the leftmost layer in this network is called the input layer, and the neurons within the layer are called input neurons. The rightmost or output layer contains the output neurons, or, as in this case, a single output neuron. The middle layer is called a hidden layer, since the neurons in this layer are neither inputs nor outputs. The term "hidden" perhaps sounds a little mysterious - the first time I heard the term I thought it must have some deep philosophical or mathematical significance - but it really means nothing more than "not an input or an output". The network above has just a single hidden layer, but some networks have multiple hidden layers. For example, the following four-layer network has two hidden layers: Somewhat confusingly, and for historical reasons, such multiple layer networks are sometimes called multilayer perceptrons or MLPs, despite being made up of sigmoid neurons, not perceptrons. I'm not going to use the MLP terminology in this book, since I think it's confusing, but wanted to warn you of its existence.The design of the input and output layers in a network is often straightforward. For example, suppose we're trying to determine whether a handwritten image depicts a "9" or not. A natural way to design the network is to encode the intensities of the image pixels into the input neurons. If the image is a 646464 by 646464 greyscale image, then we'd have 4,096=64×644,096=64×644,096 = 64 \times 64 input neurons, with the intensities scaled appropriately between 000 and 111. The output layer will contain just a single neuron, with output values of less than 0.50.50.5 indicating "input image is not a 9", and values greater than 0.50.50.5 indicating "input image is a 9 ". While the design of the input and output layers of a neural network is often straightforward, there can be quite an art to the design of the hidden layers. In particular, it's not possible to sum up the design process for the hidden layers with a few simple rules of thumb. Instead, neural networks researchers have developed many design heuristics for the hidden layers, which help people get the behaviour they want out of their nets. For example, such heuristics can be used to help determine how to trade off the number of hidden layers against the time required to train the network. We'll meet several such design heuristics later in this book. Up to now, we've been discussing neural networks where the output from one layer is used as input to the next layer. Such networks are called feedforward neural networks. This means there are no loops in the network - information is always fed forward, never fed back. If we did have loops, we'd end up with situations where the input to the σσ\sigma function depended on the output. That'd be hard to make sense of, and so we don't allow such loops.However, there are other models of artificial neural networks in which feedback loops are possible. These models are called recurrent neural networks. The idea in these models is to have neurons which fire for some limited duration of time, before becoming quiescent. That firing can stimulate other neurons, which may fire a little while later, also for a limited duration. That causes still more neurons to fire, and so over time we get a cascade of neurons firing. Loops don't cause problems in such a model, since a neuron's output only affects its input at some later time, not instantaneously.Recurrent neural nets have been less influential than feedforward networks, in part because the learning algorithms for recurrent nets are (at least to date) less powerful. But recurrent networks are still extremely interesting. They're much closer in spirit to how our brains work than feedforward networks. And it's possible that recurrent networks can solve important problems which can only be solved with great difficulty by feedforward networks. However, to limit our scope, in this book we're going to concentrate on the more widely-used feedforward networks.A simple network to classify handwritten digitsHaving defined neural networks, let's return to handwriting recognition. We can split the problem of recognizing handwritten digits into two sub-problems. First, we'd like a way of breaking an image containing many digits into a sequence of separate images, each containing a single digit. For example, we'd like to break the imageinto six separate images, We humans solve this segmentation problem with ease, but it's challenging for a computer program to correctly break up the image. Once the image has been segmented, the program then needs to classify each individual digit. So, for instance, we'd like our program to recognize that the first digit above,is a 5.We'll focus on writing a program to solve the second problem, that is, classifying individual digits. We do this because it turns out that the segmentation problem is not so difficult to solve, once you have a good way of classifying individual digits. There are many approaches to solving the segmentation problem. One approach is to trial many different ways of segmenting the image, using the individual digit classifier to score each trial segmentation. A trial segmentation gets a high score if the individual digit classifier is confident of its classification in all segments, and a low score if the classifier is having a lot of trouble in one or more segments. The idea is that if the classifier is having trouble somewhere, then it's probably having trouble because the segmentation has been chosen incorrectly. This idea and other variations can be used to solve the segmentation problem quite well. So instead of worrying about segmentation we'll concentrate on developing a neural network which can solve the more interesting and difficult problem, namely, recognizing individual handwritten digits.To recognize individual digits we will use a three-layer neural network: The input layer of the network contains neurons encoding the values of the input pixels. As discussed in the next section, our training data for the network will consist of many 282828 by 282828 pixel images of scanned handwritten digits, and so the input layer contains 784=28×28784=28×28784 = 28 \times 28 neurons. For simplicity I've omitted most of the 784784784 input neurons in the diagram above. The input pixels are greyscale, with a value of 0.00.00.0 representing white, a value of 1.01.01.0 representing black, and in between values representing gradually darkening shades of grey.The second layer of the network is a hidden layer. We denote the number of neurons in this hidden layer by nnn, and we'll experiment with different values for nnn. The example shown illustrates a small hidden layer, containing just n=15n=15n = 15 neurons.The output layer of the network contains 10 neurons. If the first neuron fires, i.e., has an output ≈1≈1\approx 1, then that will indicate that the network thinks the digit is a 000. If the second neuron fires then that will indicate that the network thinks the digit is a 111. And so on. A little more precisely, we number the output neurons from 000 through 999, and figure out which neuron has the highest activation value. If that neuron is, say, neuron number 666, then our network will guess that the input digit was a 666. And so on for the other output neurons.You might wonder why we use 101010 output neurons. After all, the goal of the network is to tell us which digit (0,1,2,…,90,1,2,…,90, 1, 2, \ldots, 9) corresponds to the input image. A seemingly natural way of doing that is to use just 444 output neurons, treating each neuron as taking on a binary value, depending on whether the neuron's output is closer to 000 or to 111. Four neurons are enough to encode the answer, since 24=1624=162^4 = 16 is more than the 10 possible values for the input digit. Why should our network use 101010 neurons instead? Isn't that inefficient? The ultimate justification is empirical: we can try out both network designs, and it turns out that, for this particular problem, the network with 101010 output neurons learns to recognize digits better than the network with 444 output neurons. But that leaves us wondering why using 101010 output neurons works better. Is there some heuristic that would tell us in advance that we should use the 101010-output encoding instead of the 444-output encoding?To understand why we do this, it helps to think about what the neural network is doing from first principles. Consider first the case where we use 101010 output neurons. Let's concentrate on the first output neuron, the one that's trying to decide whether or not the digit is a 000. It does this by weighing up evidence from the hidden layer of neurons. What are those hidden neurons doing? Well, just suppose for the sake of argument that the first neuron in the hidden layer detects whether or not an image like the following is present:It can do this by heavily weighting input pixels which overlap with the image, and only lightly weighting the other inputs. In a similar way, let's suppose for the sake of argument that the second, third, and fourth neurons in the hidden layer detect whether or not the following images are present:As you may have guessed, these four images together make up the 000 image that we saw in the line of digits shown earlier:So if all four of these hidden neurons are firing then we can conclude that the digit is a 000. Of course, that's not the only sort of evidence we can use to conclude that the image was a 000 - we could legitimately get a 000 in many other ways (say, through translations of the above images, or slight distortions). But it seems safe to say that at least in this case we'd conclude that the input was a 000.Supposing the neural network functions in this way, we can give a plausible explanation for why it's better to have 101010 outputs from the network, rather than 444. If we had 444 outputs, then the first output neuron would be trying to decide what the most significant bit of the digit was. And there's no easy way to relate that most significant bit to simple shapes like those shown above. It's hard to imagine that there's any good historical reason the component shapes of the digit will be closely related to (say) the most significant bit in the output.Now, with all that said, this is all just a heuristic. Nothing says that the three-layer neural network has to operate in the way I described, with the hidden neurons detecting simple component shapes. Maybe a clever learning algorithm will find some assignment of weights that lets us use only 444 output neurons. But as a heuristic the way of thinking I've described works pretty well, and can save you a lot of time in designing good neural network architectures.Exercise There is a way of determining the bitwise representation of a digit by adding an extra layer to the three-layer network above. The extra layer converts the output from the previous layer into a binary representation, as illustrated in the figure below. Find a set of weights and biases for the new output layer. Assume that the first 333 layers of neurons are such that the correct output in the third layer (i.e., the old output layer) has activation at least 0.990.990.99, and incorrect outputs have activation less than 0.010.010.01. Learning with gradient descentNow that we have a design for our neural network, how can it learn to recognize digits? The first thing we'll need is a data set to learn from - a so-called training data set. We'll use the MNIST data set, which contains tens of thousands of scanned images of handwritten digits, together with their correct classifications. MNIST's name comes from the fact that it is a modified subset of two data sets collected by NIST, the United States' National Institute of Standards and Technology. Here's a few images from MNIST: As you can see, these digits are, in fact, the same as those shown at the beginning of this chapter as a challenge to recognize. Of course, when testing our network we'll ask it to recognize images which aren't in the training set!The MNIST data comes in two parts. The first part contains 60,000 images to be used as training data. These images are scanned handwriting samples from 250 people, half of whom were US Census Bureau employees, and half of whom were high school students. The images are greyscale and 28 by 28 pixels in size. The second part of the MNIST data set is 10,000 images to be used as test data. Again, these are 28 by 28 greyscale images. We'll use the test data to evaluate how well our neural network has learned to recognize digits. To make this a good test of performance, the test data was taken from a different set of 250 people than the original training data (albeit still a group split between Census Bureau employees and high school students). This helps give us confidence that our system can recognize digits from people whose writing it didn't see during training.We'll use the notation xxx to denote a training input. It'll be convenient to regard each training input xxx as a 28×28=78428×28=78428 \times 28 = 784-dimensional vector. Each entry in the vector represents the grey value for a single pixel in the image. We'll denote the corresponding desired output by y=y(x)y=y(x)y = y(x), where yyy is a 101010-dimensional vector. For example, if a particular training image, xxx, depicts a 666, then y(x)=(0,0,0,0,0,0,1,0,0,0)Ty(x)=(0,0,0,0,0,0,1,0,0,0)Ty(x) = (0, 0, 0, 0, 0, 0, 1, 0, 0, 0)^T is the desired output from the network. Note that TTT here is the transpose operation, turning a row vector into an ordinary (column) vector.What we'd like is an algorithm which lets us find weights and biases so that the output from the network approximates y(x)y(x)y(x) for all training inputs xxx. To quantify how well we're achieving this goal we define a cost function* *Sometimes referred to as a loss or objective function. We use the term cost function throughout this book, but you should note the other terminology, since it's often used in research papers and other discussions of neural networks. : C(w,b)≡12n∑x∥y(x)−a∥2.(6)(6)C(w,b)≡12n∑x‖y(x)−a‖2.\begin{eqnarray} C(w,b) \equiv \frac{1}{2n} \sum_x \| y(x) - a\|^2. \tag{6}\end{eqnarray} Here, www denotes the collection of all weights in the network, bbb all the biases, nnn is the total number of training inputs, aaa is the vector of outputs from the network when xxx is input, and the sum is over all training inputs, xxx. Of course, the output aaa depends on xxx, www and bbb, but to keep the notation simple I haven't explicitly indicated this dependence. The notation ∥v∥‖v‖\| v \| just denotes the usual length function for a vector vvv. We'll call CCC the quadratic cost function; it's also sometimes known as the mean squared error or just MSE. Inspecting the form of the quadratic cost function, we see that C(w,b)C(w,b)C(w,b) is non-negative, since every term in the sum is non-negative. Furthermore, the cost C(w,b)C(w,b)C(w,b) becomes small, i.e., C(w,b)≈0C(w,b)≈0C(w,b) \approx 0, precisely when y(x)y(x)y(x) is approximately equal to the output, aaa, for all training inputs, xxx. So our training algorithm has done a good job if it can find weights and biases so that C(w,b)≈0C(w,b)≈0C(w,b) \approx 0. By contrast, it's not doing so well when C(w,b)C(w,b)C(w,b) is large - that would mean that y(x)y(x)y(x) is not close to the output aaa for a large number of inputs. So the aim of our training algorithm will be to minimize the cost C(w,b)C(w,b)C(w,b) as a function of the weights and biases. In other words, we want to find a set of weights and biases which make the cost as small as possible. We'll do that using an algorithm known as gradient descent. Why introduce the quadratic cost? After all, aren't we primarily interested in the number of images correctly classified by the network? Why not try to maximize that number directly, rather than minimizing a proxy measure like the quadratic cost? The problem with that is that the number of images correctly classified is not a smooth function of the weights and biases in the network. For the most part, making small changes to the weights and biases won't cause any change at all in the number of training images classified correctly. That makes it difficult to figure out how to change the weights and biases to get improved performance. If we instead use a smooth cost function like the quadratic cost it turns out to be easy to figure out how to make small changes in the weights and biases so as to get an improvement in the cost. That's why we focus first on minimizing the quadratic cost, and only after that will we examine the classification accuracy.Even given that we want to use a smooth cost function, you may still wonder why we choose the quadratic function used in Equation (6)C(w,b)≡12n∑x∥y(x)−a∥2C(w,b)≡12n∑x‖y(x)−a‖2\begin{eqnarray} C(w,b) \equiv \frac{1}{2n} \sum_x \| y(x) - a\|^2 \nonumber\end{eqnarray}$('#margin_501822820305_reveal').click(function() {$('#margin_501822820305').toggle('slow', function() {});});. Isn't this a rather ad hoc choice? Perhaps if we chose a different cost function we'd get a totally different set of minimizing weights and biases? This is a valid concern, and later we'll revisit the cost function, and make some modifications. However, the quadratic cost function of Equation (6)C(w,b)≡12n∑x∥y(x)−a∥2C(w,b)≡12n∑x‖y(x)−a‖2\begin{eqnarray} C(w,b) \equiv \frac{1}{2n} \sum_x \| y(x) - a\|^2 \nonumber\end{eqnarray}$('#margin_555483302348_reveal').click(function() {$('#margin_555483302348').toggle('slow', function() {});}); works perfectly well for understanding the basics of learning in neural networks, so we'll stick with it for now.Recapping, our goal in training a neural network is to find weights and biases which minimize the quadratic cost function C(w,b)C(w,b)C(w, b). This is a well-posed problem, but it's got a lot of distracting structure as currently posed - the interpretation of www and bbb as weights and biases, the σσ\sigma function lurking in the background, the choice of network architecture, MNIST, and so on. It turns out that we can understand a tremendous amount by ignoring most of that structure, and just concentrating on the minimization aspect. So for now we're going to forget all about the specific form of the cost function, the connection to neural networks, and so on. Instead, we're going to imagine that we've simply been given a function of many variables and we want to minimize that function. We're going to develop a technique called gradient descent which can be used to solve such minimization problems. Then we'll come back to the specific function we want to minimize for neural networks.Okay, let's suppose we're trying to minimize some function, C(v)C(v)C(v). This could be any real-valued function of many variables, v=v1,v2,…v=v1,v2,…v = v_1, v_2, \ldots. Note that I've replaced the www and bbb notation by vvv to emphasize that this could be any function - we're not specifically thinking in the neural networks context any more. To minimize C(v)C(v)C(v) it helps to imagine CCC as a function of just two variables, which we'll call v1v1v_1 and v2v2v_2:What we'd like is to find where CCC achieves its global minimum. Now, of course, for the function plotted above, we can eyeball the graph and find the minimum. In that sense, I've perhaps shown slightly too simple a function! A general function, CCC, may be a complicated function of many variables, and it won't usually be possible to just eyeball the graph to find the minimum.One way of attacking the problem is to use calculus to try to find the minimum analytically. We could compute derivatives and then try using them to find places where CCC is an extremum. With some luck that might work when CCC is a function of just one or a few variables. But it'll turn into a nightmare when we have many more variables. And for neural networks we'll often want far more variables - the biggest neural networks have cost functions which depend on billions of weights and biases in an extremely complicated way. Using calculus to minimize that just won't work!(After asserting that we'll gain insight by imagining CCC as a function of just two variables, I've turned around twice in two paragraphs and said, "hey, but what if it's a function of many more than two variables?" Sorry about that. Please believe me when I say that it really does help to imagine CCC as a function of two variables. It just happens that sometimes that picture breaks down, and the last two paragraphs were dealing with such breakdowns. Good thinking about mathematics often involves juggling multiple intuitive pictures, learning when it's appropriate to use each picture, and when it's not.)Okay, so calculus doesn't work. Fortunately, there is a beautiful analogy which suggests an algorithm which works pretty well. We start by thinking of our function as a kind of a valley. If you squint just a little at the plot above, that shouldn't be too hard. And we imagine a ball rolling down the slope of the valley. Our everyday experience tells us that the ball will eventually roll to the bottom of the valley. Perhaps we can use this idea as a way to find a minimum for the function? We'd randomly choose a starting point for an (imaginary) ball, and then simulate the motion of the ball as it rolled down to the bottom of the valley. We could do this simulation simply by computing derivatives (and perhaps some second derivatives) of CCC - those derivatives would tell us everything we need to know about the local "shape" of the valley, and therefore how our ball should roll.Based on what I've just written, you might suppose that we'll be trying to write down Newton's equations of motion for the ball, considering the effects of friction and gravity, and so on. Actually, we're not going to take the ball-rolling analogy quite that seriously - we're devising an algorithm to minimize CCC, not developing an accurate simulation of the laws of physics! The ball's-eye view is meant to stimulate our imagination, not constrain our thinking. So rather than get into all the messy details of physics, let's simply ask ourselves: if we were declared God for a day, and could make up our own laws of physics, dictating to the ball how it should roll, what law or laws of motion could we pick that would make it so the ball always rolled to the bottom of the valley?To make this question more precise, let's think about what happens when we move the ball a small amount Δv1Δv1\Delta v_1 in the v1v1v_1 direction, and a small amount Δv2Δv2\Delta v_2 in the v2v2v_2 direction. Calculus tells us that CCC changes as follows: ΔC≈∂C∂v1Δv1+∂C∂v2Δv2.(7)(7)ΔC≈∂C∂v1Δv1+∂C∂v2Δv2.\begin{eqnarray} \Delta C \approx \frac{\partial C}{\partial v_1} \Delta v_1 + \frac{\partial C}{\partial v_2} \Delta v_2. \tag{7}\end{eqnarray} We're going to find a way of choosing Δv1Δv1\Delta v_1 and Δv2Δv2\Delta v_2 so as to make ΔCΔC\Delta C negative; i.e., we'll choose them so the ball is rolling down into the valley. To figure out how to make such a choice it helps to define ΔvΔv\Delta v to be the vector of changes in vvv, Δv≡(Δv1,Δv2)TΔv≡(Δv1,Δv2)T\Delta v \equiv (\Delta v_1, \Delta v_2)^T, where TTT is again the transpose operation, turning row vectors into column vectors. We'll also define the gradient of CCC to be the vector of partial derivatives, (∂C∂v1,∂C∂v2)T(∂C∂v1,∂C∂v2)T\left(\frac{\partial C}{\partial v_1}, \frac{\partial C}{\partial v_2}\right)^T. We denote the gradient vector by ∇C∇C\nabla C, i.e.: ∇C≡(∂C∂v1,∂C∂v2)T.(8)(8)∇C≡(∂C∂v1,∂C∂v2)T.\begin{eqnarray} \nabla C \equiv \left( \frac{\partial C}{\partial v_1}, \frac{\partial C}{\partial v_2} \right)^T. \tag{8}\end{eqnarray} In a moment we'll rewrite the change ΔCΔC\Delta C in terms of ΔvΔv\Delta v and the gradient, ∇C∇C\nabla C. Before getting to that, though, I want to clarify something that sometimes gets people hung up on the gradient. When meeting the ∇C∇C\nabla C notation for the first time, people sometimes wonder how they should think about the ∇∇\nabla symbol. What, exactly, does ∇∇\nabla mean? In fact, it's perfectly fine to think of ∇C∇C\nabla C as a single mathematical object - the vector defined above - which happens to be written using two symbols. In this point of view, ∇∇\nabla is just a piece of notational flag-waving, telling you "hey, ∇C∇C\nabla C is a gradient vector". There are more advanced points of view where ∇∇\nabla can be viewed as an independent mathematical entity in its own right (for example, as a differential operator), but we won't need such points of view.With these definitions, the expression (7)ΔC≈∂C∂v1Δv1+∂C∂v2Δv2ΔC≈∂C∂v1Δv1+∂C∂v2Δv2\begin{eqnarray} \Delta C \approx \frac{\partial C}{\partial v_1} \Delta v_1 + \frac{\partial C}{\partial v_2} \Delta v_2 \nonumber\end{eqnarray}$('#margin_512380394946_reveal').click(function() {$('#margin_512380394946').toggle('slow', function() {});}); for ΔCΔC\Delta C can be rewritten as ΔC≈∇C⋅Δv.(9)(9)ΔC≈∇C⋅Δv.\begin{eqnarray} \Delta C \approx \nabla C \cdot \Delta v. \tag{9}\end{eqnarray} This equation helps explain why ∇C∇C\nabla C is called the gradient vector: ∇C∇C\nabla C relates changes in vvv to changes in CCC, just as we'd expect something called a gradient to do. But what's really exciting about the equation is that it lets us see how to choose ΔvΔv\Delta v so as to make ΔCΔC\Delta C negative. In particular, suppose we choose Δv=−η∇C,(10)(10)Δv=−η∇C,\begin{eqnarray} \Delta v = -\eta \nabla C, \tag{10}\end{eqnarray} where ηη\eta is a small, positive parameter (known as the learning rate). Then Equation (9)ΔC≈∇C⋅ΔvΔC≈∇C⋅Δv\begin{eqnarray} \Delta C \approx \nabla C \cdot \Delta v \nonumber\end{eqnarray}$('#margin_31741254841_reveal').click(function() {$('#margin_31741254841').toggle('slow', function() {});}); tells us that ΔC≈−η∇C⋅∇C=−η∥∇C∥2ΔC≈−η∇C⋅∇C=−η‖∇C‖2\Delta C \approx -\eta \nabla C \cdot \nabla C = -\eta \|\nabla C\|^2. Because ∥∇C∥2≥0‖∇C‖2≥0\| \nabla C \|^2 \geq 0, this guarantees that ΔC≤0ΔC≤0\Delta C \leq 0, i.e., CCC will always decrease, never increase, if we change vvv according to the prescription in (10)Δv=−η∇CΔv=−η∇C\begin{eqnarray} \Delta v = -\eta \nabla C \nonumber\end{eqnarray}$('#margin_48762573303_reveal').click(function() {$('#margin_48762573303').toggle('slow', function() {});});. (Within, of course, the limits of the approximation in Equation (9)ΔC≈∇C⋅ΔvΔC≈∇C⋅Δv\begin{eqnarray} \Delta C \approx \nabla C \cdot \Delta v \nonumber\end{eqnarray}$('#margin_919658643545_reveal').click(function() {$('#margin_919658643545').toggle('slow', function() {});});). This is exactly the property we wanted! And so we'll take Equation (10)Δv=−η∇CΔv=−η∇C\begin{eqnarray} \Delta v = -\eta \nabla C \nonumber\end{eqnarray}$('#margin_287729255111_reveal').click(function() {$('#margin_287729255111').toggle('slow', function() {});}); to define the "law of motion" for the ball in our gradient descent algorithm. That is, we'll use Equation (10)Δv=−η∇CΔv=−η∇C\begin{eqnarray} \Delta v = -\eta \nabla C \nonumber\end{eqnarray}$('#margin_718723868298_reveal').click(function() {$('#margin_718723868298').toggle('slow', function() {});}); to compute a value for ΔvΔv\Delta v, then move the ball's position vvv by that amount: v→v′=v−η∇C.(11)(11)v→v′=v−η∇C.\begin{eqnarray} v \rightarrow v' = v -\eta \nabla C. \tag{11}\end{eqnarray} Then we'll use this update rule again, to make another move. If we keep doing this, over and over, we'll keep decreasing CCC until - we hope - we reach a global minimum.Summing up, the way the gradient descent algorithm works is to repeatedly compute the gradient ∇C∇C\nabla C, and then to move in the opposite direction, "falling down" the slope of the valley. We can visualize it like this:Notice that with this rule gradient descent doesn't reproduce real physical motion. In real life a ball has momentum, and that momentum may allow it to roll across the slope, or even (momentarily) roll uphill. It's only after the effects of friction set in that the ball is guaranteed to roll down into the valley. By contrast, our rule for choosing ΔvΔv\Delta v just says "go down, right now". That's still a pretty good rule for finding the minimum!To make gradient descent work correctly, we need to choose the learning rate ηη\eta to be small enough that Equation (9)ΔC≈∇C⋅ΔvΔC≈∇C⋅Δv\begin{eqnarray} \Delta C \approx \nabla C \cdot \Delta v \nonumber\end{eqnarray}$('#margin_560455937071_reveal').click(function() {$('#margin_560455937071').toggle('slow', function() {});}); is a good approximation. If we don't, we might end up with ΔC>0ΔC>0\Delta C > 0, which obviously would not be good! At the same time, we don't want ηη\eta to be too small, since that will make the changes ΔvΔv\Delta v tiny, and thus the gradient descent algorithm will work very slowly. In practical implementations, ηη\eta is often varied so that Equation (9)ΔC≈∇C⋅ΔvΔC≈∇C⋅Δv\begin{eqnarray} \Delta C \approx \nabla C \cdot \Delta v \nonumber\end{eqnarray}$('#margin_157848846275_reveal').click(function() {$('#margin_157848846275').toggle('slow', function() {});}); remains a good approximation, but the algorithm isn't too slow. We'll see later how this works. I've explained gradient descent when CCC is a function of just two variables. But, in fact, everything works just as well even when CCC is a function of many more variables. Suppose in particular that CCC is a function of mmm variables, v1,…,vmv1,…,vmv_1,\ldots,v_m. Then the change ΔCΔC\Delta C in CCC produced by a small change Δv=(Δv1,…,Δvm)TΔv=(Δv1,…,Δvm)T\Delta v = (\Delta v_1, \ldots, \Delta v_m)^T is ΔC≈∇C⋅Δv,(12)(12)ΔC≈∇C⋅Δv,\begin{eqnarray} \Delta C \approx \nabla C \cdot \Delta v, \tag{12}\end{eqnarray} where the gradient ∇C∇C\nabla C is the vector ∇C≡(∂C∂v1,…,∂C∂vm)T.(13)(13)∇C≡(∂C∂v1,…,∂C∂vm)T.\begin{eqnarray} \nabla C \equiv \left(\frac{\partial C}{\partial v_1}, \ldots, \frac{\partial C}{\partial v_m}\right)^T. \tag{13}\end{eqnarray} Just as for the two variable case, we can choose Δv=−η∇C,(14)(14)Δv=−η∇C,\begin{eqnarray} \Delta v = -\eta \nabla C, \tag{14}\end{eqnarray} and we're guaranteed that our (approximate) expression (12)ΔC≈∇C⋅ΔvΔC≈∇C⋅Δv\begin{eqnarray} \Delta C \approx \nabla C \cdot \Delta v \nonumber\end{eqnarray}$('#margin_869505431896_reveal').click(function() {$('#margin_869505431896').toggle('slow', function() {});}); for ΔCΔC\Delta C will be negative. This gives us a way of following the gradient to a minimum, even when CCC is a function of many variables, by repeatedly applying the update rule v→v′=v−η∇C.(15)(15)v→v′=v−η∇C.\begin{eqnarray} v \rightarrow v' = v-\eta \nabla C. \tag{15}\end{eqnarray} You can think of this update rule as defining the gradient descent algorithm. It gives us a way of repeatedly changing the position vvv in order to find a minimum of the function CCC. The rule doesn't always work - several things can go wrong and prevent gradient descent from finding the global minimum of CCC, a point we'll return to explore in later chapters. But, in practice gradient descent often works extremely well, and in neural networks we'll find that it's a powerful way of minimizing the cost function, and so helping the net learn.Indeed, there's even a sense in which gradient descent is the optimal strategy for searching for a minimum. Let's suppose that we're trying to make a move ΔvΔv\Delta v in position so as to decrease CCC as much as possible. This is equivalent to minimizing ΔC≈∇C⋅ΔvΔC≈∇C⋅Δv\Delta C \approx \nabla C \cdot \Delta v. We'll constrain the size of the move so that ∥Δv∥=ϵ‖Δv‖=ϵ\| \Delta v \| = \epsilon for some small fixed ϵ>0ϵ>0\epsilon > 0. In other words, we want a move that is a small step of a fixed size, and we're trying to find the movement direction which decreases CCC as much as possible. It can be proved that the choice of ΔvΔv\Delta v which minimizes ∇C⋅Δv∇C⋅Δv\nabla C \cdot \Delta v is Δv=−η∇CΔv=−η∇C\Delta v = - \eta \nabla C, where η=ϵ/∥∇C∥η=ϵ/‖∇C‖\eta = \epsilon / \|\nabla C\| is determined by the size constraint ∥Δv∥=ϵ‖Δv‖=ϵ\|\Delta v\| = \epsilon. So gradient descent can be viewed as a way of taking small steps in the direction which does the most to immediately decrease CCC.Exercises Prove the assertion of the last paragraph. Hint: If you're not already familiar with the Cauchy-Schwarz inequality, you may find it helpful to familiarize yourself with it. I explained gradient descent when CCC is a function of two variables, and when it's a function of more than two variables. What happens when CCC is a function of just one variable? Can you provide a geometric interpretation of what gradient descent is doing in the one-dimensional case? People have investigated many variations of gradient descent, including variations that more closely mimic a real physical ball. These ball-mimicking variations have some advantages, but also have a major disadvantage: it turns out to be necessary to compute second partial derivatives of CCC, and this can be quite costly. To see why it's costly, suppose we want to compute all the second partial derivatives ∂2C/∂vj∂vk∂2C/∂vj∂vk\partial^2 C/ \partial v_j \partial v_k. If there are a million such vjvjv_j variables then we'd need to compute something like a trillion (i.e., a million squared) second partial derivatives* *Actually, more like half a trillion, since ∂2C/∂vj∂vk=∂2C/∂vk∂vj∂2C/∂vj∂vk=∂2C/∂vk∂vj\partial^2 C/ \partial v_j \partial v_k = \partial^2 C/ \partial v_k \partial v_j. Still, you get the point.! That's going to be computationally costly. With that said, there are tricks for avoiding this kind of problem, and finding alternatives to gradient descent is an active area of investigation. But in this book we'll use gradient descent (and variations) as our main approach to learning in neural networks.How can we apply gradient descent to learn in a neural network? The idea is to use gradient descent to find the weights wkwkw_k and biases blblb_l which minimize the cost in Equation (6)C(w,b)≡12n∑x∥y(x)−a∥2C(w,b)≡12n∑x‖y(x)−a‖2\begin{eqnarray} C(w,b) \equiv \frac{1}{2n} \sum_x \| y(x) - a\|^2 \nonumber\end{eqnarray}$('#margin_1246306310_reveal').click(function() {$('#margin_1246306310').toggle('slow', function() {});});. To see how this works, let's restate the gradient descent update rule, with the weights and biases replacing the variables vjvjv_j. In other words, our "position" now has components wkwkw_k and blblb_l, and the gradient vector ∇C∇C\nabla C has corresponding components ∂C/∂wk∂C/∂wk\partial C / \partial w_k and ∂C/∂bl∂C/∂bl\partial C / \partial b_l. Writing out the gradient descent update rule in terms of components, we have wkbl→→w′k=wk−η∂C∂wkb′l=bl−η∂C∂bl.(16)(17)(16)wk→wk′=wk−η∂C∂wk(17)bl→bl′=bl−η∂C∂bl.\begin{eqnarray} w_k & \rightarrow & w_k' = w_k-\eta \frac{\partial C}{\partial w_k} \tag{16}\\ b_l & \rightarrow & b_l' = b_l-\eta \frac{\partial C}{\partial b_l}. \tag{17}\end{eqnarray} By repeatedly applying this update rule we can "roll down the hill", and hopefully find a minimum of the cost function. In other words, this is a rule which can be used to learn in a neural network.There are a number of challenges in applying the gradient descent rule. We'll look into those in depth in later chapters. But for now I just want to mention one problem. To understand what the problem is, let's look back at the quadratic cost in Equation (6)C(w,b)≡12n∑x∥y(x)−a∥2C(w,b)≡12n∑x‖y(x)−a‖2\begin{eqnarray} C(w,b) \equiv \frac{1}{2n} \sum_x \| y(x) - a\|^2 \nonumber\end{eqnarray}$('#margin_214093216664_reveal').click(function() {$('#margin_214093216664').toggle('slow', function() {});});. Notice that this cost function has the form C=1n∑xCxC=1n∑xCxC = \frac{1}{n} \sum_x C_x, that is, it's an average over costs Cx≡∥y(x)−a∥22Cx≡‖y(x)−a‖22C_x \equiv \frac{\|y(x)-a\|^2}{2} for individual training examples. In practice, to compute the gradient ∇C∇C\nabla C we need to compute the gradients ∇Cx∇Cx\nabla C_x separately for each training input, xxx, and then average them, ∇C=1n∑x∇Cx∇C=1n∑x∇Cx\nabla C = \frac{1}{n} \sum_x \nabla C_x. Unfortunately, when the number of training inputs is very large this can take a long time, and learning thus occurs slowly.An idea called stochastic gradient descent can be used to speed up learning. The idea is to estimate the gradient ∇C∇C\nabla C by computing ∇Cx∇Cx\nabla C_x for a small sample of randomly chosen training inputs. By averaging over this small sample it turns out that we can quickly get a good estimate of the true gradient ∇C∇C\nabla C, and this helps speed up gradient descent, and thus learning.To make these ideas more precise, stochastic gradient descent works by randomly picking out a small number mmm of randomly chosen training inputs. We'll label those random training inputs X1,X2,…,XmX1,X2,…,XmX_1, X_2, \ldots, X_m, and refer to them as a mini-batch. Provided the sample size mmm is large enough we expect that the average value of the ∇CXj∇CXj\nabla C_{X_j} will be roughly equal to the average over all ∇Cx∇Cx\nabla C_x, that is, ∑mj=1∇CXjm≈∑x∇Cxn=∇C,(18)(18)∑j=1m∇CXjm≈∑x∇Cxn=∇C,\begin{eqnarray} \frac{\sum_{j=1}^m \nabla C_{X_{j}}}{m} \approx \frac{\sum_x \nabla C_x}{n} = \nabla C, \tag{18}\end{eqnarray} where the second sum is over the entire set of training data. Swapping sides we get ∇C≈1m∑j=1m∇CXj,(19)(19)∇C≈1m∑j=1m∇CXj,\begin{eqnarray} \nabla C \approx \frac{1}{m} \sum_{j=1}^m \nabla C_{X_{j}}, \tag{19}\end{eqnarray} confirming that we can estimate the overall gradient by computing gradients just for the randomly chosen mini-batch. To connect this explicitly to learning in neural networks, suppose wkwkw_k and blblb_l denote the weights and biases in our neural network. Then stochastic gradient descent works by picking out a randomly chosen mini-batch of training inputs, and training with those, wkbl→→w′k=wk−ηm∑j∂CXj∂wkb′l=bl−ηm∑j∂CXj∂bl,(20)(21)(20)wk→wk′=wk−ηm∑j∂CXj∂wk(21)bl→bl′=bl−ηm∑j∂CXj∂bl,\begin{eqnarray} w_k & \rightarrow & w_k' = w_k-\frac{\eta}{m} \sum_j \frac{\partial C_{X_j}}{\partial w_k} \tag{20}\\ b_l & \rightarrow & b_l' = b_l-\frac{\eta}{m} \sum_j \frac{\partial C_{X_j}}{\partial b_l}, \tag{21}\end{eqnarray} where the sums are over all the training examples XjXjX_j in the current mini-batch. Then we pick out another randomly chosen mini-batch and train with those. And so on, until we've exhausted the training inputs, which is said to complete an epoch of training. At that point we start over with a new training epoch.Incidentally, it's worth noting that conventions vary about scaling of the cost function and of mini-batch updates to the weights and biases. In Equation (6)C(w,b)≡12n∑x∥y(x)−a∥2C(w,b)≡12n∑x‖y(x)−a‖2\begin{eqnarray} C(w,b) \equiv \frac{1}{2n} \sum_x \| y(x) - a\|^2 \nonumber\end{eqnarray}$('#margin_85851492824_reveal').click(function() {$('#margin_85851492824').toggle('slow', function() {});}); we scaled the overall cost function by a factor 1n1n\frac{1}{n}. People sometimes omit the 1n1n\frac{1}{n}, summing over the costs of individual training examples instead of averaging. This is particularly useful when the total number of training examples isn't known in advance. This can occur if more training data is being generated in real time, for instance. And, in a similar way, the mini-batch update rules (20)wk→w′k=wk−ηm∑j∂CXj∂wkwk→wk′=wk−ηm∑j∂CXj∂wk\begin{eqnarray} w_k & \rightarrow & w_k' = w_k-\frac{\eta}{m} \sum_j \frac{\partial C_{X_j}}{\partial w_k} \nonumber\end{eqnarray}$('#margin_801900730537_reveal').click(function() {$('#margin_801900730537').toggle('slow', function() {});}); and (21)bl→b′l=bl−ηm∑j∂CXj∂blbl→bl′=bl−ηm∑j∂CXj∂bl\begin{eqnarray} b_l & \rightarrow & b_l' = b_l-\frac{\eta}{m} \sum_j \frac{\partial C_{X_j}}{\partial b_l} \nonumber\end{eqnarray}$('#margin_985072620111_reveal').click(function() {$('#margin_985072620111').toggle('slow', function() {});}); sometimes omit the 1m1m\frac{1}{m} term out the front of the sums. Conceptually this makes little difference, since it's equivalent to rescaling the learning rate ηη\eta. But when doing detailed comparisons of different work it's worth watching out for.We can think of stochastic gradient descent as being like political polling: it's much easier to sample a small mini-batch than it is to apply gradient descent to the full batch, just as carrying out a poll is easier than running a full election. For example, if we have a training set of size n=60,000n=60,000n = 60,000, as in MNIST, and choose a mini-batch size of (say) m=10m=10m = 10, this means we'll get a factor of 6,0006,0006,000 speedup in estimating the gradient! Of course, the estimate won't be perfect - there will be statistical fluctuations - but it doesn't need to be perfect: all we really care about is moving in a general direction that will help decrease CCC, and that means we don't need an exact computation of the gradient. In practice, stochastic gradient descent is a commonly used and powerful technique for learning in neural networks, and it's the basis for most of the learning techniques we'll develop in this book.Exercise An extreme version of gradient descent is to use a mini-batch size of just 1. That is, given a training input, xxx, we update our weights and biases according to the rules wk→w′k=wk−η∂Cx/∂wkwk→wk′=wk−η∂Cx/∂wkw_k \rightarrow w_k' = w_k - \eta \partial C_x / \partial w_k and bl→b′l=bl−η∂Cx/∂blbl→bl′=bl−η∂Cx/∂blb_l \rightarrow b_l' = b_l - \eta \partial C_x / \partial b_l. Then we choose another training input, and update the weights and biases again. And so on, repeatedly. This procedure is known as online, on-line, or incremental learning. In online learning, a neural network learns from just one training input at a time (just as human beings do). Name one advantage and one disadvantage of online learning, compared to stochastic gradient descent with a mini-batch size of, say, 202020. Let me conclude this section by discussing a point that sometimes bugs people new to gradient descent. In neural networks the cost CCC is, of course, a function of many variables - all the weights and biases - and so in some sense defines a surface in a very high-dimensional space. Some people get hung up thinking: "Hey, I have to be able to visualize all these extra dimensions". And they may start to worry: "I can't think in four dimensions, let alone five (or five million)". Is there some special ability they're missing, some ability that "real" supermathematicians have? Of course, the answer is no. Even most professional mathematicians can't visualize four dimensions especially well, if at all. The trick they use, instead, is to develop other ways of representing what's going on. That's exactly what we did above: we used an algebraic (rather than visual) representation of ΔCΔC\Delta C to figure out how to move so as to decrease CCC. People who are good at thinking in high dimensions have a mental library containing many different techniques along these lines; our algebraic trick is just one example. Those techniques may not have the simplicity we're accustomed to when visualizing three dimensions, but once you build up a library of such techniques, you can get pretty good at thinking in high dimensions. I won't go into more detail here, but if you're interested then you may enjoy reading this discussion of some of the techniques professional mathematicians use to think in high dimensions. While some of the techniques discussed are quite complex, much of the best content is intuitive and accessible, and could be mastered by anyone. Implementing our network to classify digitsAlright, let's write a program that learns how to recognize handwritten digits, using stochastic gradient descent and the MNIST training data. We'll do this with a short Python (2.7) program, just 74 lines of code! The first thing we need is to get the MNIST data. If you're a git user then you can obtain the data by cloning the code repository for this book,git clone https://github.com/mnielsen/neural-networks-and-deep-learning.git If you don't use git then you can download the data and code here.Incidentally, when I described the MNIST data earlier, I said it was

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More Copy link to Tweet Embed Tweet The FBI said it has stopped using the "Black Identity Extremist" tag and acknowledged that white supremacist violence is the biggest terrorist threat this country faces.

The FBI said it has stopped using the "Black Identity Extremist" tag and acknowledged that white supremacist violence is the biggest terrorist threat this country faces. https://trib.al/OepGw2S

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5) The selective function. This refers not to human choice at all but to Darwin's theory of natural selection as applied to what he called "the favored races." In short, the idea is to help things along by consciously attempting to improve the breeding stock. Schools are meant to tag the unfit - with poor grades, remedial placement, and other punishments - clearly enough that their peers will accept them as inferior and effectively bar them from the reproductive sweepstakes. That's what all those little humiliations from first grade onward were intended to do: wash the dirt down the drain.

The FBI said it has stopped using the "Black Identity Extremist" tag and acknowledged that white supremacist violence is the biggest terrorist threat this country faces.

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Aufwühlende Zeiten sind das für Annegret Kramp-Karrenbauer. Als die auch AKK genannte CDU-Bundesvorsitzende am Montagabend beim Ständehaustreff, einer Live-Talkshow der „Rheinischen Post“, in Düsseldorf auf die Bühne kommt, hat sie einen langen Tag mit Gremiensitzungen in Berlin mit vielen wichtigen Klärungen hinter sich.

The FBI said it has stopped using the "Black Identity Extremist" tag and acknowledged that white supremacist violence is the biggest terrorist threat this country faces.

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