The best commit messages I’ve seen don’t just explain what they’ve changed: they explain why

If you use practices like pair or mob programming, don't forget to add your coworkers names in your commit messages

$ git commit -m "Refactor usability tests.
>
>
Co-authored-by: name <name@example.com>
Co-authored-by: another-name <another-name@example.com>"

I'm fond of gitmoji commit convention. It lies on categorizing commits using emojies. I'm a visual person so it fits well to me but I understand this convention is not made for everyone.

:recycle: Make core independent from the git client (#171)
:whale: Upgrade Docker image version (#167)

♻️ Make core independent from the git client (#171)
🐳 Upgrade Docker image version (#167)

Separate subject from body with a blank line Limit the subject line to 50 characters Capitalize the subject line Do not end the subject line with a period Use the imperative mood in the subject line Wrap the body at 72 characters Use the body to explain what and why vs. how

Don’t commit directly to the master or development branches. Don’t hold up work by not committing local branch changes to remote branches. Never commit application secrets in public repositories. Don’t commit large files in the repository. This will increase the size of the repository. Use Git LFS for large files.  Learn more about what Git LFS is and how to utilize it in this advanced Learning Git with GitKraken tutorial. Don’t create one pull request addressing multiple issues. Don’t work on multiple issues in the same branch. If a feature is dropped, it will be difficult to revert changes. Don’t reset a branch without committing/stashing your changes. If you do so, your changes will be lost. Don’t do a force push until you’re extremely comfortable performing this action. Don’t modify or delete public history. 

Create a Git repository for every new project. Learn more about what a Git repo is in this beginner Learning Git with GitKraken tutorial. Always create a new branch for every new feature and bug. Regularly commit and push changes to the remote branch to avoid loss of work. Include a gitignore file in your project to avoid unwanted files being committed. Always commit changes with a concise and useful commit message.  Utilize git-submodule for large projects. Keep your branch up to date with development branches. Follow a workflow like Gitflow. There are many workflows available, so choose the one that best suits your needs. Always create a pull request for merging changes from one branch to another. Learn more about what a pull request is and how to create them in this intermediate Learning Git with GitKraken tutorial. Always create one pull request addressing one issue. Always review your code once by yourself before creating a pull request. Have more than one person review a pull request. It’s not necessary, but is a best practice. Enforce standards by using pull request templates and adding continuous integrations. Learn more about enhancing the pull request process with templates.  Merge changes from the release branch to master after each release. Tag the master sources after every release. Delete branches if a feature or bug fix is merged to its intended branches and the branch is no longer required. Automate general workflow checks using Git hooks. Learn more about how to trigger Git hooks in this intermediate Learning Git with GitKraken tutorial. Include read/write permission access control to repositories to prevent unauthorized access. Add protection for special branches like master and development to safeguard against accidental deletion.

To add the .gitattributes to the repo first you need to create a file called .gitattributes into the root folder for the repo.

*.js    eol=lf
*.jsx   eol=lf
*.json  eol=lf

On the Windows machine the default for the line ending is a Carriage Return Line Feed (CRLF), whereas on Linux/MacOS it's a Line Feed (LF).

{
  "endOfLine": "lf"
}

The above commands will now update the files for the repo using the newly defined line ending as specified in the .gitattributes.

git rm --cached -r .
git reset --hard

alias g=git

alias g=git

dictionary (a piece of structured data) can be converted into n different possible documents (XML, PDF, paper or otherwise), where n is the number of possible permutations of the elements in the dictionary

The correct way to express a dictionary in XML is something like this

<root>
  <item>
    <key>Name</key>
    <value>John</value>
  </item>
  <item>
    <key>City</key>
    <value>London</value>
  </item>
</root>

Broadly speaking, XML excels at annotating corpuses of text with structure and metadata

XML has no notion of numbers (or booleans, or other data types), any numbers represented are just considered more text

To date, the only XML schemas I have seen which I would actually consider a good use of XML are XHTML and DocBook

UI layouts are represented as component trees. And XML is ideal for representing tree structures. It’s a match made in heaven! In fact, the most popular UI frameworks in the world (HTML and Android) use XML syntax to define layouts.

XML may not be ideal to represent generic data structures, but it excels at representing one particular structure: the tree. By separating node data (attributes) from parent/child relationships, the tree structure of the data shines through, and the code to process the data can be quite elegant.

I feel great that all of my posts are now safely saved in version control and markdown. It’s a relief for me to know that they’re no longer an HTML mess inside of a MySQL database, but markdown files which are easy to read, write, edit, share, and backup.

However, I realized that a static site generator like Gatsby utilizes the power of code/data splitting, pre-loading, pre-caching, image optimization, and all sorts of performance enhancements that would be difficult or impossible to do with straight HTML.

I’ll give you the basics of what I did in case you also want to make the switch.

A few things I really like about Gatsby

I had over 100 guides and tutorials to migrate, and in the end I was able to move everything in 10 days, so it was far from the end of the world.

There are a lot of static site generators to choose from. Jekyll, Hugo, Next, and Hexo are some of the big ones, and I’ve heard of and looked into some interesting up-and-coming SSGs like Eleventy as well.

There is a good amount of prerequisite knowledge required to set up a Gatsby site - HTML, CSS, JavaScript, ES6, Node.js development environment, React, and GraphQL are the major ones.

Gatsby is SEO friendly – it is part of the JAMStack after all!

Gatsby is a React based framework which utilises the powers of Webpack and GraphQL to bundle real React components into static HTML, CSS and JS files. Gatsby can be plugged into and used straight away with any data source you have available, whether that is your own API, Database or CMS backend (Spoiler Alert!).

The combination of WordPress, React, Gatsby and GraphQL is just that - fun

A: Read an article from start to finish. ONLY THEN do you import parts into Anki for remembering B: Incremental Reading: interleaving between reading and remembering

“I think SM is only good for a small minority of learners. But they will probably value it very much.”

In Anki, you are only doing the remembering part. You are not reading anything new in Anki

Using either SRS has already given you a huge edge over not using any SRS: No SRS: 70 hours Anki: 10 hours SuperMemo: 6 hours The difference between using any SRS (whether it’s Anki or SM) and not using is huge, but the difference between Anki or SM is not

“Anki is a tool and SuperMemo is a lifestyle.”

The Cornell Note-taking System

And for the last three years, I've added EVERYTHING to Anki. Bash aliases, IDE Shortcuts, programming APIs, documentation, design patterns, etc. Having done that, I wouldn't recommend adding EVERYTHING

Habit: Whenever I search StackOverflow, I'll immediately create a flashcard of my question and the answer(s) into Anki.

The confidence of knowing that once something is added to Anki it won't be forgotten is intoxicating

Kyle had a super hero ability. Photographic memory in API syntax and documentation. I wanted that and I was jealous. My career was stuck and something needed to change. And so I began a dedicated journey into spaced repetition. Every day for three years, I spent one to three hours in spaced repetition

First up, regular citizens who download copyrighted content from illegal sources will not be criminalized. This means that those who obtain copies of the latest movies from the Internet, for example, will be able to continue doing so without fear of reprisals. Uploading has always been outlawed and that aspect has not changed.

The point of nbdev is to bring the key benefits of IDE/editor development into the notebook system, so you can work in notebooks without compromise for the entire lifecycle

Disaggregated model means the storage system sees data as a collection of objects or files. But end users are not interested in the physical arrangement of data, they instead want to see a more logical view of their data.

ACID stands for Atomicity (an operation either succeeds completely or fails, it does not leave partial data), Consistency (once an application performs an operation the results of that operation are visible to it in every subsequent operation), Isolation (an incomplete operation by one user does not cause unexpected side effects for other users), and Durability (once an operation is complete it will be preserved even in the face of machine or system failure).

Currently this may be possible using version management of object store, but that as we saw earlier is at a lower layer of physical detail which may not be useful at higher, logical level.

Traditionally Data Warehouse tools were used to drive business intelligence from data. Industry then recognized that Data Warehouses limit the potential of intelligence by enforcing schema on write. It was clear that all the dimensions of data-set being collected could not be thought of at the time of data collection.

As explained above, users are no longer willing to consider inefficiencies of underlying platforms. For example, data lakes are now also expected to be ACID compliant, so that the end user doesn’t have the additional overhead of ensuring data related guarantees.

Commonly used Storage platforms are object storage platforms like AWS S3, Azure Blob Storage, GCS, Ceph, MinIO among others. While analytics platforms vary from simple Python & R based notebooks to Tensorflow to Spark, Presto to Splunk, Vertica and others.

Data Lakes that are optimized for unstructured and semi-structured data, can scale to PetaBytes easily and allowed better integration of a wide range of tools to help businesses get the most out of their data.

'Directed' means that the edges of the graph only move in one direction, where future edges are dependent on previous ones.

Several cryptocurrencies use DAGs rather than blockchain data structures in order to process and validate transactions.

graph data structure that uses topological ordering, meaning that the graph flows in only one direction, and it never goes in circles.

'Acyclic' means that it is impossible to start at one point of the graph and come back to it by following the edges.

when AUC is 0.5, it means model has no class separation capacity whatsoever.

ROC is a probability curve and AUC represents degree or measure of separability. It tells how much model is capable of distinguishing between classes.

In multi-class model, we can plot N number of AUC ROC Curves for N number classes using One vs ALL methodology. So for Example, If you have three classes named X, Y and Z, you will have one ROC for X classified against Y and Z, another ROC for Y classified against X and Z, and a third one of Z classified against Y and X.

When AUC is approximately 0, model is actually reciprocating the classes. It means, model is predicting negative class as a positive class and vice versa

AUC near to the 1 which means it has good measure of separability.

AWS offers instances with Terabytes of RAM. In this case you still have to manage cloud data buckets, wait for data transfer from bucket to instance every time the instance starts, handle compliance issues that come with putting data on the cloud, and deal with all the inconvenience that come with working on a remote machine. Not to mention the costs, which although start low, tend to pile up as time goes on.

LR is nothing but the binomial regression with logit link (or probit), one of the numerous GLM cases. As a regression - itself it doesn't classify anything, it models the conditional (to linear predictor) expected value of the Bernoulli/binomially distributed DV.

BOW is often used for Natural Language Processing (NLP) tasks like Text Classification. Its strengths lie in its simplicity: it’s inexpensive to compute, and sometimes simpler is better when positioning or contextual info aren’t relevant

Notice that we lose contextual information, e.g. where in the document the word appeared, when we use BOW. It’s like a literal bag-of-words: it only tells you what words occur in the document, not where they occurred

Here’s my preferred way of doing it, which uses Keras’s Tokenizer class

vectors here have length 7 instead of 6 because of the extra 0 element at the beginning. This is an inconsequential detail - Keras reserves index 0 and never assigns it to any word.

process that takes input data through a series of transformation stages, producing data as output

Softmax turns arbitrary real values into probabilities

1. Logistic regression IS a binomial regression (with logit link), a special case of the Generalized Linear Model. It doesn't classify anything *unless a threshold for the probability is set*. Classification is just its application. 2. Stepwise regression is by no means a regression. It's a (flawed) method of variable selection. 3. OLS is a method of estimation (among others: GLS, TLS, (RE)ML, PQL, etc.), NOT a regression. 4. Ridge, LASSO - it's a method of regularization, NOT a regression. 5. There are tens of models for the regression analysis. You mention mainly linear and logistic - it's just the GLM! Learn the others too (link in a comment). STOP with the "17 types of regression every DS should know". BTW, there're 270+ statistical tests. Not just t, chi2 & Wilcoxon

400 sized probability sample (a small random sample from the whole population) is often better than a millions sized administrative sample (of the kind you can download from gov sites). The reason is that an arbitrary sample (as opposed to a random one) is very likely to be biased, and, if large enough, a confidence interval (which actually doesn't really make sense except for probability samples) will be so narrow that, because of the bias, it will actually rarely, if ever, include the true value we are trying to estimate. On the other hand, the small, random sample will be very likely to include the true value in its (wider) confidence interval

An exploratory plot is all about you getting to know the data. An explanatory graphic, on the other hand, is about telling a story using that data to a specific audience.

Here’s a very simple example of how a VQA system might answer the question “what color is the triangle?”

Visual Question Answering (VQA): answering open-ended questions about images. VQA is interesting because it requires combining visual and language understanding.

Most VQA models would use some kind of Recurrent Neural Network (RNN) to process the question input

The standard approach to performing VQA looks something like this: Process the image. Process the question. Combine features from steps 1/2. Assign probabilities to each possible answer.

Script mode takes a function/class, reinterprets the Python code and directly outputs the TorchScript IR. This allows it to support arbitrary code, however it essentially needs to reinterpret Python

In 2019, the war for ML frameworks has two remaining main contenders: PyTorch and TensorFlow. My analysis suggests that researchers are abandoning TensorFlow and flocking to PyTorch in droves. Meanwhile in industry, Tensorflow is currently the platform of choice, but that may not be true for long

Why do researchers love PyTorch?

Researchers care about how fast they can iterate on their research, which is typically on relatively small datasets (datasets that can fit on one machine) and run on <8 GPUs. This is not typically gated heavily by performance considerations, but by their ability to quickly implement new ideas. On the other hand, industry considers performance to be of the utmost priority. While 10% faster runtime means nothing to a researcher, that could directly translate to millions of savings for a company

From the early academic outputs Caffe and Theano to the massive industry-backed PyTorch and TensorFlow

When you run a PyTorch/TensorFlow model, most of the work isn’t actually being done in the framework itself, but rather by third party kernels. These kernels are often provided by the hardware vendor, and consist of operator libraries that higher-level frameworks can take advantage of. These are things like MKLDNN (for CPU) or cuDNN (for Nvidia GPUs). Higher-level frameworks break their computational graphs into chunks, which can then call these computational libraries. These libraries represent thousands of man hours of effort, and are often optimized for the architecture and application to yield the best performance

Jax is built by the same people who built the original Autograd, and features both forward- and reverse-mode auto-differentiation. This allows computation of higher order derivatives orders of magnitude faster than what PyTorch/TensorFlow can offer

TensorFlow will always have a captive audience within Google/DeepMind, but I wonder whether Google will eventually relax this

Once your PyTorch model is in this IR, we gain all the benefits of graph mode. We can deploy PyTorch models in C++ without a Python dependency , or optimize it.

At their core, PyTorch and Tensorflow are auto-differentiation frameworks

At the API level, TensorFlow eager mode is essentially identical to PyTorch’s eager mode, originally made popular by Chainer. This gives TensorFlow most of the advantages of PyTorch’s eager mode (ease of use, debuggability, and etc.) However, this also gives TensorFlow the same disadvantages. TensorFlow eager models can’t be exported to a non-Python environment, they can’t be optimized, they can’t run on mobile, etc. This puts TensorFlow in the same position as PyTorch, and they resolve it in essentially the same way - you can trace your code (tf.function) or reinterpret the Python code (Autograph).

TensorFlow came out years before PyTorch, and industry is slower to adopt new technologies than researchers

The PyTorch JIT is an intermediate representation (IR) for PyTorch called TorchScript. TorchScript is the “graph” representation of PyTorch. You can turn a regular PyTorch model into TorchScript by using either tracing or script mode.

In 2018, PyTorch was a minority. Now, it is an overwhelming majority, with 69% of CVPR using PyTorch, 75+% of both NAACL and ACL, and 50+% of ICLR and ICML

Tracing takes a function and an input, records the operations that were executed with that input, and constructs the IR. Although straightforward, tracing has its downsides. For example, it can’t capture control flow that didn’t execute. For example, it can’t capture the false block of a conditional if it executed the true block

On the other hand, industry has a litany of restrictions/requirements

TensorFlow is still the dominant framework. For example, based on data [2] [3] from 2018 to 2019, TensorFlow had 1541 new job listings vs. 1437 job listings for PyTorch on public job boards, 3230 new TensorFlow Medium articles vs. 1200 PyTorch, 13.7k new GitHub stars for TensorFlow vs 7.2k for PyTorch, etc

every major conference in 2019 has had a majority of papers implemented in PyTorch

the transition from TensorFlow 1.0 to 2.0 will be difficult and provides a natural point for companies to evaluate PyTorch

For the application of machine learning in finance, it’s still very early days. Some of the stuff people have been doing in finance for a long time is simple machine learning, and some people were using neural networks back in the 80s and 90s.   But now we have a lot more data and a lot more computing power, so with our creativity in machine learning research, “We are so much in the beginning that we can’t even picture where we’re going to be 20 years from now”

ability to learn from data e.g. OpenAI and the Rubik’s Cube and DeepMind with AlphaGo required the equivalent of thousands of years of gameplay to achieve those milestones

Pedro’s book “The Master Algorithm” takes readers on a journey through the five dominant paradigms of machine learning research on a quest for the master  algorithm. Along the way, Pedro wanted to abstract away from the mechanics so that a broad audience, from the CXO to the consumer, can understand how machine learning is shaping our lives

We’ve always lived in a world which we didn’t completely understand but now we’re living in a world designed by us – for Pedro, that’s actually an improvement

But at the end of the day, what we know about neuroscience today is not enough to determine what we do in AI, it’s only enough to give us ideas.  In fact it’s a two way street – AI can help us to learn how the brain works and this loop between the two disciplines is a very important one and is growing very rapidly

Pedro believes that success will come from unifying the different major types of learning and their master algorithms –not just combining, but unifying them such that “it feels like using one thing”

if you look at the number  of connections that the state of the art machine learning systems for some of these problems have, they’re more than many animals – they have many hundreds of millions or billions of connections

There was this period of a couple of 100 years where we understood our technology.  Now we just have to learn live in a world where we don’t understand the machines that work  for us, we just have to be confident they are working for us and doing their best

team began its analysis on YouTube 8M, a publicly available dataset of YouTube videos

The trailer release for a new movie is a highly anticipated event that can help predict future success, so it behooves the business to ensure the trailer is hitting the right notes with moviegoers. To achieve this goal, the 20th Century Fox data science team partnered with Google’s Advanced Solutions Lab to create Merlin Video, a computer vision tool that learns dense representations of movie trailers to help predict a specific trailer’s future moviegoing audience

pipeline also includes a distance-based “collaborative filtering” (CF) model and a logistic regression layer that combines all the model outputs together to produce the movie attendance probability

Merlin returns the following labels: facial_hair, beard, screenshot, chin, human, film

The obvious choice was Cloud Machine Learning Engine (Cloud ML Engine), in conjunction with the TensorFlow deep learning framework

custom model learns the temporal sequencing of labels in the movie trailer

The elasticity of Cloud ML Engine allowed the data science team to iterate and test quickly, without compromising the integrity of the deep learning model

Architecture flow diagram for Merlin

The first challenge is the temporal position of the labels in the trailer: it matters when the labels occur in the trailer. The second challenge is the high dimensionality of this data

When it comes to movies, analyzing text taken from a script is limiting because it only provides a skeleton of the story, without any of the additional dynamism that can entice an audience to see a movie

20th Century Fox has been using this tool since the release of The Greatest Showman in 2017, and continues to use it to inform their latest releases

model is trained end-to-end, and the loss of the logistic regression is back-propagated to all the trainable components (weights). Merlin’s data pipeline is refreshed weekly to account for new trailer releases

After a movie’s release, we are able to process the data on which movies were previously seen by that audience. The table below shows the top 20 actual moviegoer audiences (Comp ACT) compared to the top 20 predicted audiences (Comp PRED)

It’s possible to check whether a variable refers to it with the comparison operators == and !=

>>> x, y = 2, None
>>> x == None
False
>>> y == None
True
>>> x != None
True
>>> y != None
False

>>> x is None
False
>>> y is None
True
>>> x is not None
True
>>> y is not None
False

Python allows defining getter and setter methods similarly as C++ and Java

>>> class C:
...     def get_x(self):
...         return self.__x
...     def set_x(self, value):
...         self.__x = value

>>> c = C()
>>> c.set_x(2)
>>> c.get_x()
2

In almost all cases, you can use the range to get an iterator that yields integers

>>> x = [1, 2, 4, 8, 16]
>>> for i in range(len(x)):
...     print(x[i])
... 
1
2
4
8
16

>>> for item in x:
...     print(item)
... 
1
2
4
8
16

Sometimes you need both the items from a sequence and the corresponding indices

>>> for i in range(len(x)):
...     print(i, x[i])
... 
0 1
1 2
2 4
3 8
4 16

>>> for i, item in enumerate(x):
...     print(i, item)
... 
0 1
1 2
2 4
3 8
4 16

But what if you want to iterate in the reversed order? Of course, the range is an option again

>>> for i in range(len(x)-1, -1, -1):
...     print(x[i])
... 
16
8
4
2
1

>>> for item in x[::-1]:
...     print(item)
... 
16
8
4
2
1

>>> for item in reversed(x):
...     print(item)
... 
16
8
4
2
1

it’s often more elegant to define and use properties, especially in simple cases

>>> class C:
...     @property
...     def x(self):
...         return self.__x
...     @x.setter
...     def x(self, value):
...         self.__x = value

>>> c = C()
>>> c.x = 2
>>> c.x
2

Python has a very flexible system of providing arguments to functions and methods. Optional arguments are a part of this offer. But be careful: you usually don’t want to use mutable optional arguments

>>> def f(value, seq=[]):
...     seq.append(value)
...     return seq

>>> f(value=2)
[2]

>>> f(value=4)
[2, 4]
>>> f(value=8)
[2, 4, 8]
>>> f(value=16)
[2, 4, 8, 16]

Iterating over a dictionary yields its keys

>>> z = {'a': 0, 'b': 1}
>>> for k in z:
... print(k, z[k])
... 
a 0
b 1

>>> for k, v in z.items():
...     print(k, v)
... 
a 0
b 1

following the rules called The Zen of Python or PEP 20

You can use unpacking to assign values to your variables

>>> a, b = 2, 'my-string'
>>> a
2
>>> b
'my-string'

what if you want to iterate over two or more sequences? Of course, you can use the range again

>>> y = 'abcde'
>>> for i in range(len(x)):
...     print(x[i], y[i])
... 
1 a
2 b
4 c
8 d
16 e

>>> for item in zip(x, y):
...     print(item)
... 
(1, 'a')
(2, 'b')
(4, 'c')
(8, 'd')
(16, 'e')

>>> for x_item, y_item in zip(x, y):
...     print(x_item, y_item)
... 
1 a
2 b
4 c
8 d
16 e

None is a special and unique object in Python. It has a similar purpose, like null in C-like languages

Python allows you to chain the comparison operations. So, you don’t have to use and to check if two or more comparisons are True

>>> x = 4
>>> x >= 2 and x <= 8
True

>>> 2 <= x <= 8
True
>>> 2 <= x <= 3
False

Unpacking can be used for the assignment to multiple variables in more complex cases

>>> x = (1, 2, 4, 8, 16)
>>> a = x[0]
>>> b = x[1]
>>> c = x[2]
>>> d = x[3]
>>> e = x[4]
>>> a, b, c, d, e
(1, 2, 4, 8, 16)

>>> a, b, c, d, e = x
>>> a, b, c, d, e
(1, 2, 4, 8, 16)

>>> a, *y, e = x
>>> a, e, y
(1, 16, [2, 4, 8])

the most concise and elegant variables swap

>>> a, b = b, a
>>> a
'my-string'
>>> b
2

Python doesn’t have real private class members. However, there’s a convention that says that you shouldn’t access or modify the members beginning with the underscore (_) outside their instances. They are not guaranteed to preserve the existing behavior.

>>> class C:
...     def __init__(self, *args):
...         self.x, self._y, self.__z = args
... 
>>> c = C(1, 2, 4)

>>> c.x  # OK
1

>>> c._y  # Possible, but a bad practice!
2

>>> c.__z # Error!
Traceback (most recent call last):
File "", line 1, in 
AttributeError: 'C' object has no attribute '__z'
>>> c._C__z # Possible, but even worse!
4
>>>

what if an exception occurs while processing your file? Then my_file.close() is never executed. You can handle this with exception-handling syntax or with context managers

>>> with open('filename.csv', 'w') as my_file:
...     # do something with `my_file`

Python code should be elegant, concise, and readable. It should be beautiful. The ultimate resource on how to write beautiful Python code is Style Guide for Python Code or PEP 8

Python also supports chained assignments. So, if you want to assign the same value to multiple variables, you can do it in a straightforward way

>>> x = 2
>>> y = 2
>>> z = 2

>>> x, y, z = 2, 2, 2

>>> x = y = z = 2
>>> x, y, z
(2, 2, 2)

open a file and process it

>>> my_file = open('filename.csv', 'w')
>>> # do something with `my_file`

>>> my_file = open('filename.csv', 'w')
>>> # do something with `my_file and`
>>> my_file.close()

The author of the class probably begins the names with the underscore(s) to tell you, “don’t use it”

You can keep away from that with some additional logic. One of the ways is this:

>>> def f(value, seq=[]):
...     seq.append(value)
...     return seq

>>> def f(value, seq=None):
...     if seq is None:
...         seq = []
...     seq.append(value)
...     return seq

>>> def f(value, seq=None):
...     if not seq:
...         seq = []
...     seq.append(value)
...     return seq

>>> f(value=2)
[2]
>>> f(value=4)
[4]
>>> f(value=8)
[8]
>>> f(value=16)
[16]

The Pythonic way is to exploit the fact that zero is interpreted as False in a Boolean context, while all other numbers are considered as True

>>> bool(0)
False
>>> bool(-1), bool(1), bool(20), bool(28.4)
(True, True, True, True)

>>> for item in x:
...     if item:
...         print(item)
... 
1
2
3
4

When you have numeric data, and you need to check if the numbers are equal to zero, you can but don’t have to use the comparison operators == and !=

>>> x = (1, 2, 0, 3, 0, 4)
>>> for item in x:
...     if item != 0:
...         print(item)
... 
1
2
3
4

it’s a good practice to use properties when you can and C++-like getters and setters when you have to

The reason that Julia is fast (ten to 30 times faster than Python) is because it is compiled and not interpreted

Scala is ten times faster than Python

First of all, write a script that carries out the task in a sequential fashion. Secondly, transform the script so that it carries out the task using the map function. Lastly, replace map with a neat function from the concurrent.futures module

Python standard library makes it fairly easy to create threads and processes

Python is a poor choice for concurrent programming. A principal reason for this is the ‘Global Interpreter Lock’ or GIL. The GIL ensures that only one thread accesses Python objects at a time, effectively preventing Python from being able to distribute threads onto several CPUs by default

Introducing multiprocessing now is a cinch; I just replace ThreadPoolExecutor with ProcessPoolExecutor in the previous listing

this article merely scratches the surface. If you want to dig deeper into concurrency in Python, there is an excellent talk titled Thinking about Concurrency by Raymond Hettinger on the subject. Make sure to also check out the slides whilst you’re at it

As the name suggests, multiprocessing spawns processes, while multithreading spawns threads. In Python, one process can run several threads. Each process has its proper Python interpreter and its proper GIL. As a result, starting a process is a heftier and more time-consuming undertaking than starting a thread.

its purpose is to dump Python tracebacks explicitly on a fault, after a timeout, or on a user signal

what parts of the software do we profile (measure its performance metrics)

Line profiling, as the name suggests, means to profile your Python code line by line

The profile module gives similar results with similar commands. Typically, you switch to profile if cProfile isn’t available

Another common component to profile is the memory usage. The purpose is to find memory leaks and optimize the memory usage in your Python programs

The purpose of trace module is to “monitor which statements and functions are executed as a program runs to produce coverage and call-graph information

method profiling tool like cProfile (which is available in the Python language), the timing metrics for methods can show you statistics, such as the number of calls (shown as ncalls), total time spent in the function (tottime), time per call (tottime/ncalls and shown as percall), cumulative time spent in a function (cumtime), and cumulative time per call (quotient of cumtime over the number of primitive calls and shown as percall after cumtime)

tracing is a special use case of logging in order to record information about a program’s execution

If a method has an acceptable speed but is so frequently called that it becomes a huge time sink, you would want to know this from your profiler

trace and faulthandler modules cover basic tracing

It’s just that it often makes sense to write code in the order JOIN / WHERE / GROUP BY / HAVING. (I’ll often put a WHERE first to improve performance though, and I think most database engines will also do a WHERE first in practice)

# Creating a 5x5 matrix arr = [[i for i in range(5)] for j in range(5)] arr >>> [[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]]

arr = [[i for i in range(5)] for j in range(5)]
arr
>>> [[0, 1, 2, 3, 4],
 [0, 1, 2, 3, 4],
 [0, 1, 2, 3, 4],
 [0, 1, 2, 3, 4],
 [0, 1, 2, 3, 4]]

x = [2,45,21,45] y = {i:v for i,v in enumerate(x)} print(y) >>> {0: 2, 1: 45, 2: 21, 3: 45}

x = [2,45,21,45]
y = {i:v for i,v in enumerate(x)}
print(y)
>>> {0: 2, 1: 45, 2: 21, 3: 45}

x = [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]] arr = [i for j in x for i in j] print(arr) >>> [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

x = [[0, 1, 2, 3, 4],
 [5, 6, 7, 8, 9]]

arr = [i for j in x for i in j]
print(arr)
>>> [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

arr = [i for i in range(10) if i % 2 == 0] print(arr) >>> [0, 2, 4, 6, 8] arr = ["Even" if i % 2 == 0 else "Odd" for i in range(10)] print(arr) >>> ['Even', 'Odd', 'Even', 'Odd', 'Even', 'Odd', 'Even', 'Odd', 'Even', 'Odd']

arr = [i for i in range(10) if i % 2 == 0]
print(arr)
>>> [0, 2, 4, 6, 8]

arr = ["Even" if i % 2 == 0 else "Odd" for i in range(10)]
print(arr)
>>> ['Even', 'Odd', 'Even', 'Odd', 'Even', 'Odd', 'Even', 'Odd', 'Even', 'Odd']

use pyenv. With it, you will be able to have any version you want at your disposal, very easy.

the __ methods allow us to interact with core concepts of the python language. You can see them also as a mechanism of implementing behaviours, interface methods.

Dunder or magic method, are methods that start and end with double _ like __init__ or __str__. This kind of methods are the mechanism we use to interact directly with python's data model

Dutch programmer Guido van Rossum designed Python in 1991, naming it after the British television comedy Monty Python's Flying Circus because he was reading the show's scripts at the time.

supporting this field is extremely easy If you keep raw data, it's just a matter of adding a getter method to the Article class.

@property
def highlights(self) -> Sequence[Highlight]:
    default = [] # defensive to handle older export formats that had no annotations
    jsons = self.json.get('annotations', default)
    return list(map(Highlight, jsons))

query language doesn't necessarily mean a database. E.g. see pandas which is capable of what SQL is capable of, and even more convenient than SQL for our data exploration purposes.

cachew lets you cache function calls into an sqlite database on your disk in a matter of single decorator (similar to functools.lru_cache). The difference from functools.lru_cache is that cached data is persisted between program runs, so next time you call your function, it will only be a matter of reading from the cache.

We only need to use global keyword in a function if we want to do assignments / change them. global is not needed for printing and accessing.

# This function modifies global variable 's' 
def f(): 
    global s 
    print s 
    s = "Look for Geeksforgeeks Python Section"
    print s  

# Global Scope 
s = "Python is great!" 
f() 
print s

The nonlocal keyword is used to work with variables inside nested functions, where the variable should not belong to the inner function. Use the keyword nonlocal to declare that the variable is not local.

def myfunc1():
  x = "John"
  def myfunc2():
    nonlocal x
    x = "hello"
  myfunc2()
  return x

print(myfunc1())

Here, I rewrite this example using the walrus operator

count = fresh_fruit.get('lemon', 0)
if count:
...

if count := fresh_fruit.get('lemon', 0):
...

map, filter and reduce can all operate independently, or be chained together

During each loop of reduce, you can get result of the last loop, and the next element in the array Change the result, then return it for the next loop iteration When you're done, you have the completed collection

This lets you transform each element into something new (or keep it the same) Types don't have to be the same: can return objects, string, numbers - anything!

It loops over the elements, passing each one to a callback function You can return true to include that element in the new array, or false to exclude it

So why are map, filter and reduce useful?

The formula China is using is current total deaths/current confirmed cases.

Each year the flu infects between 2 to 11% of the population. 11The infectiousness of a disease is measured by its R0 value. R0 is a measure of how many people will catch a disease from one infected person. The R0 value of the flu is 1.28. The R0 of COVID-19 is assumed to be between 1.4–3.8

cases outside of china are doubling roughly every 5 days. The reported rate of infection in China is lower, but China has taken drastic quarantine measures, including welding people inside apartment buildings.

“confirmed cases” is the lowest possible number of cases in that country. It’s the lower bound of an estimate.

In The Atlantic, Marc Lipsitch, a leading epidemiologist at Harvard reported that “that within the coming year, some 40 to 70 percent of people around the world will be infected with the virus that causes COVID-19”. This was last week and while ridiculed at the time, his assumptions are now the generally accepted position among epidemiologists.

Death Rate

The Virus may persist on surfaces for up to 9 days, on some surfaces up to 27 days

10-50 minutes in natural spaces was the most effective to improve mood, focus and physiological markers like blood pressure and heart rate. “It’s not that there’s a decline after 50 minutes, but rather that the physiological and self-reported psychological benefits tend to plateau after that,”

Steven Hoober shed light on the three ways users hold their phones.

We’re mapping a human health tragedy that may get way worse before it subsides. Do we really want the map to be screaming bright red? Red is a very emotive colour. It has meaning. It can easily connotate danger, and death, which is still statistically extremely rare for coronavirus.

you cannot map totals using a choropleth thematic mapping technique. The reason is simple. Each of the areas on the map is a different size, and has a different number of people in it.

Learn how to write good emails, how to present your work, how to phrase your questions in a thoughtful manner.

I want to have a choice whether to forget or remember events, and I'd like to be able to potentially access forgotten ones.

Even ignoring quality of life you are looking at a 3-7 fold return on every minute you spend exercising in extended life,[1] perhaps even exceeding that if you are making optimal use of your time. Something just clicked and I was consistent since reading this. Even 1-fold return would worth it: basically you gain free consciousness (quality of thinking is shit when I exercise, but it's better than nothing).

Exobrain, or "second brain", or "brain dump" is something like public wiki where you can keep your notes, ideas and thoughts.

Annotation is the process of adding a layer of personal (with a potential for sharing) metadata on top of existing content such as highlights, notes, anchors etc.

Human brains seem to be best for generating new ideas. I want to learn more, think faster, distract less, interact and visualize, effortlessly remember everything; not memorize and do routine information processing, which computers seem better at.

Extended mind is the idea that your mind isn't limited by your physical body, but augmented by external means. Paper notes & diaries, data in your computer/phone/cloud – all these things contribute to your cognitive process, thinking and help you navigate the landscape of ideas. One of the biggest motivations to start dumping my brain out here, write and share is to offload my mind a bit and interact with people even in my physical absence.

I can't imagine going on a run without my HR monitor, because whatever the benefit exercise has and however exhausting run would be, at least I'll have a data point. learning about routine and trying to optimize routine is also way more fun than doing the actual routine Human body is fragile and needs constant care, but it's still a fascinating mechanism.

quantified self refers both to the cultural phenomenon of self-tracking with technology and to a community of users and makers of self-tracking tools who share an interest in “self-knowledge through numbers.”[1] Quantified Self practices overlap with the practice of lifelogging and other trends that incorporate technology and data acquisition into daily life, often with the goal of improving physical, mental, and/or emotional performance.

Its automated reasoning sounds great

In Grakn, these algorithms are built-in as native functionalities of the language.

code below instantiates a client, opens a session, and runs an insertion query

We will use the python client API to interact with Grakn.

We just created a Grakn keyspace experiment and defined its schema.

Now that we have the schema ready, the next step is to load it into Grakn.

docker exec -ti grakn bash -c '/grakn-core-all-linux/grakn console --keyspace experiment --file /grakn-core-all-linux/server/db/schema.gql'

Loading: /grakn-core-all-linux/server/db/schema.gql
...
{}
Successful commit: schema.gql

An attribute can be abstract if you never assign it directly and use it only as a parent type. Entities can be abstract, too, if they are never instantiated.