Efficient way to loop over Pandas Dataframe to make dummy variables (1 or 0 input)

Engineering Challenges

To generate a response, the dialogue manager follows a three-stepprocedure. First, it uses all response models to generate a set of candidate responses. Second, if thereexists apriorityresponse in the set of candidate responses (i.e. a response which takes precedenceover other responses), this response will be returned by the system.5For example, for the question"What is your name?", the response"I am an Alexa Prize socialbot"is a priority response. Third, ifthere are nopriorityresponses, the response is selected by themodel selection policy. For example,themodel selection policymay select a response by scoring all candidate responses and picking thehighest-scored response.

There are 22 response models in the system, including retrieval-based neural networks, generation-based neural networks, knowledge base question answering systems and template-based systems.Examples of candidate model responses are shown in Tabl

Early work on dialogue systems (Weizenbaum 1966, Colby 1981, Aust et al. 1995, McGlashan et al.1992, Simpson & Eraser 1993) were based mainly on states and rules hand-crafted by human experts.Modern dialogue systems typically follow a hybrid architecture, combining hand-crafted states andrules with statistical machine learning algorithms (Suendermann-Oeft et al. 2015, Jurˇcíˇcek et al.2014, Bohus et al. 2007, Williams 2011).

We present MILABOT: a deep reinforcement learning chatbot developed by theMontreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prizecompetition. MILABOT is capable of conversing with humans on popular smalltalk topics through both speech and text. The system consists of an ensemble ofnatural language generation and retrieval models, including template-based models,bag-of-words models, sequence-to-sequence neural network and latent variableneural network models. By applying reinforcement learning to crowdsourced dataand real-world user interactions, the system has been trained to select an appropriateresponse from the models in its ensemble. The system has been evaluated throughA/B testing with real-world users, where it performed significantly better thanmany competing systems. Due to its machine learning architecture, the system islikely to improve with additional data

10 Exciting Ideas of 2018 in NLP

Phrase-Based & Neural Unsupervised Machine Translation (EMNLP 2018):  T

What I will call the model (M) which is our previous neural net. It can now be seen as a low-level network. It is sometimes called an optimizee or a learner. The weights of the model are the ■ on the drawings.The optimizer (O) or meta-learner is a higher-level model which is updating the weights of the lower-level network (the model). The weights of the optimizer are the ★ on the drawings

From zero to research — An introduction to Meta-learning

10ExcitingIdeasof2018inNLP

Thesemodels are often shared globally by all worker nodes,which must frequently accesses the shared parameters asthey perform computation to refine it.

Algorithms Using Map-Reduce

Finding Similar Items

Algorithms Using Map-Reduce

Distributed File Systems

Bonferroni’s Principle

Computational Approaches to Modeling

Dynamic Structure

Specialized Hardware Implementations of Deep Network

These large models learn some functionf(x), but do so using many moreparameters than are necessary for the task.

Large-Scale Distributed Implementations

GPU Implementations

Fast CPU Implementations

Deep learning is based on the philosophy of connectionism

Selecting Hyperparameters

Determining Whether to Gather More Data

design process

Duringday-to-day development of machine learning systems, practitioners need to decidewhether to gather more data, increase or decrease model capacity, add or removeregularizing features, improve the optimization of a model, improve approximateinference in a model, or debug the software implementation of the mod

TREE BOOSTING IN A NUTSHELL

WakeOnLan

pandas.get_dummies

Why do I sometime get a crash/freeze with n_jobs > 1 under OSX or Linux?¶

Multiprocessing scikit-learn

Does scikit-learn support parallelism?

sql的优化相关问题

How can I install Windows after I've installed Ubuntu?

To modify an existing template

Shortcuts for testing out small code snippets in IntelliJ IDEA?

What exactly is Spring for?

Java Web Application Tutorial for complete beginner [closed]

There are two key differences between imap/imap_unordered and map/map_async:

N = pool.map(partial(func, b=second_arg), a_args)

62 down vote accepted My initial thought was to use partial, and as J.F. Sebastian indicated, partial works in this instance in Python >=2.7, so I am posting this, with the caveat that it won't work in 2.6. Also note that in the above code, you're passing the result of harvester(text, case) instead of the function harvester itself. Also, you aren't returning anything; you'll have to return something in order for this to be useful. I'm assuming that text is the variable that should be mapped, while case supplies the mapping function with extra information about the whole sequence. This simply maps each element in case to case[i] + case[0]. That's a bit different from what you did, but I find this example clearer: from functools import partial def harvester(text, case): X = case[0] return text + str(X) partial_harvester = partial(harvester, case=RAW_DATASET) if __name__ == '__main__': pool = multiprocessing.Pool(processes=6) case_data = RAW_DATASET pool.map(partial_harvester, case_data, 1) pool.close() pool.join()

Spark Performance Analysis

Project Tungsten: Bringing Apache Spark Closer to Bare Metal

Introducing Apache Spark Datasets

How do I load a file from resource folder?

An introduction to JSON support in Spark SQL

scala, guidelines on return type - when prefer seq, iterable, traversable

How to read files from resources folder in Scala? var ados = ados || {}; ados.run = ados.run || []; ados.run.push(function () { ados_add_placement(22,8277,"adzerk172466415",4).setZone(43); });

“Large data” work flows using pandas

High performance database joins with pandas DataFrame, more benchmarks

Copyright (C) 2009, David Beazley,http://www.dabeaz.comA Curious Course on Coroutines and Concurrency

Simple Python parallelism

BUG: DataFrame outer merge changes key columns from int64 to float64

NaN can't be stored in an integer array. This is a known limitation of pandas at the moment; I have been waiting for progress to be made with NA values in NumPy (similar to NAs in R), but it will be at least 6 months to a year before NumPy gets these features, it seems:

How to drop rows of Pandas dataframe whose value of certain column is NaN

df[['two', 'three']] = df[['two', 'three']].astype(float)

You can use pd.to_numeric (introduced in version 0.17) to convert a column or a Series to a numeric type. The function can also be applied over multiple columns of a DataFrame using apply. Importantly, the function also takes an errors key word argument that lets you force not-numeric values to be NaN, or simply ignore columns containing these values.

18 down vote accepted As I felt similarly confused with .transform operation vs. .apply I found a few answers shedding some light on the issue. This answer for example was very helpful. My takeout so far is that .transform will work (or deal) with Series (columns) in isolation from each other. What this means is that in your last two calls: df.groupby('A').transform(lambda x: (x['C'] - x['D'])) df.groupby('A').transform(lambda x: (x['C'] - x['D']).mean()) You asked .transform to take values from two columns and 'it' actually does not 'see' both of them at the same time (so to speak). transform will look at the dataframe columns one by one and return back a series (or group of series) 'made' of scalars which are repeated len(input_column) times.

transform is not that well documented, but it seems that the way it works is that what the transform function is passed is not the entire group as a dataframe, but a single column of a single group. I don't think it's really meant for what you're trying to do, and your solution with apply is fine. So suppose tips.groupby('smoker').transform(func). There will be two groups, call them group1 and group2. The transform does not call func(group1) and func(group2). Instead, it calls func(group1['total_bill']), then func(group1['tip']), etc., and then func(group2['total_bill']), func(group2['total_bill']). Here

Tags In Jekyll

Interesting Synergy trick

The Machine Learning Framework

In [1]: %load_ext autoreload In [2]: %autoreload 2 In [3]: from foo import some_function

import site; site.getsitepackages()

down vote accepted http://dev.mysql.com/doc/refman/5.1/en/alter-table.html ALTER TABLE tablename MODIFY columnname INTEGER;

宜佳

How do I see what character set a MySQL database / table / column is?

MySQL会出现中文乱码

Adding timestamps to terminal prompts?

How to disable the “unlock your keyring” popup?

A Quick Guide to Using the MySQL APT Repository

Dropout is an extremely effective, simple and recently introduced regularization technique by Srivastava et al. in Dropout: A Simple Way to Prevent Neural Networks from Overfitting (pdf) that complements the other methods (L1, L2, maxnorm).

Do Deep Nets Really Need to be Deep?***Draft for NIPS 2014 (not camera ready copy)

As an aside, in practice it is often the case that 3-layer neural networks will outperform 2-layer nets, but going even deeper (4,5,6-layer) rarely helps much more.

Representational power

N-layer neural network, we do not count the input laye

he most common layer type is the fully-connected layer in which neurons between two adjacent layers are fully pairwise connected, but neurons within a single layer share no connections.

TLDR: “What neuron type should I use?” Use the ReLU non-linearity, be careful with your learning rates and possibly monitor the fraction of “dead” units in a network. If this concerns you, give Leaky ReLU or Maxout a try. Never use sigmoid. Try tanh, but expect it to work worse than ReLU/Maxout.

One of the most striking facts about neural networks is that they can compute any function at all.

A Few Useful Things to Know about Machine Learning

Intuitive understanding of backpropagation

Multiclass SVM loss for the i-th example is then formalized as follows

Dropout: A Simple Way to Prevent Neural Networks fromOvertting

There are four key ideas behind ConvNets that take advantage of the properties of natural signals: local connections, shared weights, pooling and the use of many layers.

half-spaces sepa-rated by a hyperplane19.

Deep learning

The key aspect of deep learning is that these layers of features are not designed by human engineers: they are learned from data using a general-purpose learning procedure.

most practitioners use a procedure called stochastic gradient descent (SGD).

, The chain rule of derivatives tells us how two small effects (that of a small change of x on y, and that of y on z) are composed.

The backpropagation procedure to compute the gradient of an objective function with respect to the weights of a multilayer stack of modules is nothing more than a practical application of the chain rule for derivatives.

pip install -i http://mirrors.aliyun.com/pypi/simple pymysql

Clonezilla The Free and Open Source Software for Disk Imaging and Cloning

amyhaber/cnki-downloader

Run-length encoding (RLE)

根据评论区 @山丹丹@啸王 的提醒，更正了一些错误（用斜体显示），在此谢谢各位。并根据自己最近的理解，增添了一些东西（用斜体显示）。如果还有错误，欢迎大家指正。第一个问题：为什么引入非线性激励函数？如果不用激励函数（其实相当于激励函数是f(x) = x），在这种情况下你每一层输出都是上层输入的线性函数，很容易验证，无论你神经网络有多少层，输出都是输入的线性组合，与没有隐藏层效果相当，这种情况就是最原始的感知机（Perceptron）了。正因为上面的原因，我们决定引入非线性函数作为激励函数，这样深层神经网络就有意义了（不再是输入的线性组合，可以逼近任意函数）。最早的想法是sigmoid函数或者tanh函数，输出有界，很容易充当下一层输入（以及一些人的生物解释balabala）。第二个问题：为什么引入Relu呢？第一，采用sigmoid等函数，算激活函数时（指数运算），计算量大，反向传播求误差梯度时，求导涉及除法，计算量相对大，而采用Relu激活函数，整个过程的计算量节省很多。第二，对于深层网络，sigmoid函数反向传播时，很容易就会出现梯度消失的情况（在sigmoid接近饱和区时，变换太缓慢，导数趋于0，这种情况会造成信息丢失，参见 @Haofeng Li 答案的第三点），从而无法完成深层网络的训练。第三，Relu会使一部分神经元的输出为0，这样就造成了网络的稀疏性，并且减少了参数的相互依存关系，缓解了过拟合问题的发生（以及一些人的生物解释balabala）。当然现在也有一些对relu的改进，比如prelu，random relu等，在不同的数据集上会有一些训练速度上或者准确率上的改进，具体的大家可以找相关的paper看。多加一句，现在主流的做法，会在做完relu之后，加一步batch normalization，尽可能保证每一层网络的输入具有相同的分布[1]。而最新的paper[2]，他们在加入bypass connection之后，发现改变batch normalization的位置会有更好的效果。大家有兴趣可以看下。