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<body class=" postShowScreen browser-opera os-windows is-withMagicUnderlines is-noJs" itemscope="">
[https://unsupervisedmethods.com?source=logo-lo_65070e0c639b---604640a9497a ]
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[https://twitter.com/RobbieAllen ]
[https://medium.com/m/signin?redirect=https%3A%2F%2Funsupervisedmethods.com%2Fover-150-of-the-best-machine-learning-nlp-and-python-tutorials-ive-found-ffce2939bd78%3F_utm_source%3D1-2-2 Sign in / Sign up]
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[https://unsupervisedmethods.com Home]
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<li class="metabar-navItem js-collectionNavItem u-uiTextMedium u-fontSize14 u-inlineBlock u-textUppercase u-letterSpacing003 u-textColorNormal u-xs-paddingRight12 u-xs-marginRight0 u-paddingTop5 u-xs-paddingTop10">
[https://unsupervisedmethods.com/tagged/startup Startups]
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[https://unsupervisedmethods.com/tagged/artificial-intelligence Artificial Intelligence]
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[https://unsupervisedmethods.com/about-robbie-4fa74f42751a About Robbie]
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[http://twitter.com/robbieallen Twitter]
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[https://unsupervisedmethods.com/search ]
</li>
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<main role="main">
<article class=" u-sizeViewHeightMin100 u-overflowHidden postArticle postArticle--full is-withAccentColors u-marginBottom40" lang="en">
<header class="container u-maxWidth740">
[https://unsupervisedmethods.com/@robbieallen?source=post_header_lockup ]
[https://unsupervisedmethods.com/@robbieallen?source=post_header_lockup Robbie Allen]
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Startup Founder & Author turned PhD Student @UNCCS focused on Artificial Intelligence. Founder & Chairman @AInsights. Writing at http://unsupervisedmethods.com
<time datetime="2017-06-26T18:54:38.543Z">
Jun 26
</time>
</header>
<section class="section section--body section--first section--last" name="ccc6">
<hr class="section-divider"/>
<h1 class="graf graf--h3 graf--leading graf--title" id="181c" name="181c">
Over 150 of the Best Machine Learning, NLP, and Python Tutorials I’ve Found
</h1>
<p class="graf graf--p graf-after--h3" id="318d" name="318d">
<em class="markup--em markup--p-em">
If you like this article, check out another by Robbie:
<br/>
</em>
[https://unsupervisedmethods.com/the-ai-entrepreneurs-moral-dilemma-12b988f18cd0 The AI Entrepreneur’s Moral Dilemma]
</p>
<figure class="graf graf--figure graf-after--p" id="5b91" name="5b91">
https://cdn-images-1.medium.com/freeze/max/30/1*R8bEkSHE65EpgwmA_H0ABA.png?q=20 [[Image:1_R8bEkSHE65EpgwmA_H0ABA_1440.png]]
https://cdn-images-1.medium.com/max/800/1*R8bEkSHE65EpgwmA_H0ABA.png [[Image:1_R8bEkSHE65EpgwmA_H0ABA_7107.png]]
<figcaption class="imageCaption">
<em class="markup--em markup--figure-em">
Einstein’s desk a few hours after his death. Source: LIFE Magazine
</em>
</figcaption>
</figure>
<p class="graf graf--p graf-after--figure" id="4e09" name="4e09">
While
<strong class="markup--strong markup--p-strong">
machine learning
</strong>
has a
[https://en.wikipedia.org/wiki/Machine_learning#History_and_relationships_to_other_fields rich history]
dating back to
[https://en.wikipedia.org/wiki/Arthur_Samuel 1959]
, the field is evolving at an unprecedented rate. In a
[https://unsupervisedmethods.com/why-artificial-intelligence-is-different-from-previous-technology-waves-764d7710df8b recent article]
, I discussed why the broader artificial intelligence field is booming and likely will for some time to come. Those interested in learning ML may find it daunting to get started.
</p>
<p class="graf graf--p graf-after--p" id="8140" name="8140">
As
[https://medium.com/@robbieallen/from-ceo-to-student-2c7ae6c31812 I prepare to start my Ph.D. program]
in the Fall, I’ve been scouring the web for good resources on all aspects of machine learning and NLP. Typically, I’ll find an interesting tutorial or video, and that leads to three or four more tutorials or videos, and before I know it, I have 20 tabs of new material I need to go through. (On a side note,
[https://www.tabbundler.com/ Tab Bundler]
has been helpful to stay organized.)
</p>
<p class="graf graf--p graf-after--p" id="4626" name="4626">
<strong class="markup--strong markup--p-strong">
<em class="markup--em markup--p-em">
After finding over 25 ML-related “cheat sheets”,
</em>
</strong>
[https://unsupervisedmethods.com/cheat-sheet-of-machine-learning-and-python-and-math-cheat-sheets-a4afe4e791b6 I created a post]
<strong class="markup--strong markup--p-strong">
<em class="markup--em markup--p-em">
that links to all the good ones.
</em>
</strong>
</p>
<p class="graf graf--p graf-after--p" id="502d" name="502d">
To help others that are going through a similar discovery process, I’ve put together a list of the best
<strong class="markup--strong markup--p-strong">
<em class="markup--em markup--p-em">
tutorial
</em>
</strong>
content that I’ve found so far. It’s by no means an exhaustive list of every ML-related tutorial on the web — that would be overwhelming and duplicative. Plus, there is a bunch of mediocre content out there. My goal was to link to the best tutorials I found on the important subtopics within machine learning and NLP.
</p>
<p class="graf graf--p graf-after--p" id="35dd" name="35dd">
By
<strong class="markup--strong markup--p-strong">
<em class="markup--em markup--p-em">
tutorial
</em>
</strong>
, I’m referring to introductory content that is intending to teach a concept succinctly. I’ve avoided including chapters of books, which have a greater breadth of coverage, and research papers, which generally don’t do a good job in teaching concepts. Why not just buy a book? Tutorials are helpful when you’re trying to learn a specific niche topic or want to get different perspectives.
</p>
<p class="graf graf--p graf-after--p" id="9032" name="9032">
I’ve split this post into four sections:
<strong class="markup--strong markup--p-strong">
Machine Learning
</strong>
,
<strong class="markup--strong markup--p-strong">
NLP
</strong>
,
<strong class="markup--strong markup--p-strong">
Python
</strong>
, and
<strong class="markup--strong markup--p-strong">
Math
</strong>
. I’ve included a sampling of topics within each section, but given the vastness of the material, I can’t possibly include every possible topic.
</p>
<p class="graf graf--p graf-after--p" id="cf3d" name="cf3d">
<strong class="markup--strong markup--p-strong">
<em class="markup--em markup--p-em">
For future posts, I may create a similar list of books, online videos, and code repos as I’m compiling a growing collection of those resources too.
</em>
</strong>
</p>
<p class="graf graf--p graf-after--p" id="5c01" name="5c01">
If there are
<strong class="markup--strong markup--p-strong">
<em class="markup--em markup--p-em">
good
</em>
</strong>
tutorials you are aware of that I’m missing, please let me know! I’m trying to limit each topic to five or six tutorials since much beyond that would be repetitive. Each link should have different material from the other links or present information in a different way (e.g. code versus slides versus long-form) or from a different perspective.
</p>
<h3 class="graf graf--h3 graf-after--p" id="ae70" name="ae70">
Machine Learning
</h3>
<p class="graf graf--p graf-after--h3" id="7a06" name="7a06">
[https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471 Machine Learning is Fun!]
(medium.com/@ageitgey)
</p>
<p class="graf graf--p graf-after--p" id="614c" name="614c">
Machine Learning Crash Course:
[https://ml.berkeley.edu/blog/2016/11/06/tutorial-1/ Part I]
,
[https://ml.berkeley.edu/blog/2016/12/24/tutorial-2/ Part II]
,
[https://ml.berkeley.edu/blog/2017/02/04/tutorial-3/ Part III]
(Machine Learning at Berkeley)
</p>
<p class="graf graf--p graf-after--p" id="87bc" name="87bc">
[https://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples]
(toptal.com)
</p>
<p class="graf graf--p graf-after--p" id="dfae" name="dfae">
[https://monkeylearn.com/blog/a-gentle-guide-to-machine-learning/ A Gentle Guide to Machine Learning]
(monkeylearn.com)
</p>
<p class="graf graf--p graf-after--p" id="5585" name="5585">
[https://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/ Which machine learning algorithm should I use?]
(sas.com)
</p>
<h4 class="graf graf--h4 graf-after--p" id="c6ec" name="c6ec">
Activation and Loss Functions
</h4>
<p class="graf graf--p graf-after--h4" id="63ba" name="63ba">
[http://neuralnetworksanddeeplearning.com/chap1.html#sigmoid_neurons Sigmoid neurons]
(neuralnetworksanddeeplearning.com)
</p>
<p class="graf graf--p graf-after--p" id="da83" name="da83">
[https://www.quora.com/What-is-the-role-of-the-activation-function-in-a-neural-network What is the role of the activation function in a neural network?]
(quora.com)
</p>
<p class="graf graf--p graf-after--p" id="6bd8" name="6bd8">
[https://stats.stackexchange.com/questions/115258/comprehensive-list-of-activation-functions-in-neural-networks-with-pros-cons Comprehensive list of activation functions in neural networks with pros/cons]
(stats.stackexchange.com)
</p>
<p class="graf graf--p graf-after--p" id="b136" name="b136">
[https://medium.com/towards-data-science/activation-functions-and-its-types-which-is-better-a9a5310cc8f Activation functions and it’s types-Which is better?]
(medium.com)
</p>
<p class="graf graf--p graf-after--p" id="af51" name="af51">
[http://www.exegetic.biz/blog/2015/12/making-sense-logarithmic-loss/ Making Sense of Logarithmic Loss]
(exegetic.biz)
</p>
<p class="graf graf--p graf-after--p" id="f708" name="f708">
[http://cs231n.github.io/neural-networks-2/#losses Loss Functions]
(Stanford CS231n)
</p>
<p class="graf graf--p graf-after--p" id="0e61" name="0e61">
[http://rishy.github.io/ml/2015/07/28/l1-vs-l2-loss/ L1 vs. L2 Loss function]
(rishy.github.io)
</p>
<p class="graf graf--p graf-after--p" id="0df0" name="0df0">
[http://neuralnetworksanddeeplearning.com/chap3.html#the_cross-entropy_cost_function The cross-entropy cost function]
(neuralnetworksanddeeplearning.com)
</p>
<h4 class="graf graf--h4 graf-after--p" id="0e8a" name="0e8a">
Bias
</h4>
<p class="graf graf--p graf-after--h4" id="45cb" name="45cb">
[https://stackoverflow.com/questions/2480650/role-of-bias-in-neural-networks/2499936#2499936 Role of Bias in Neural Networks]
(stackoverflow.com)
</p>
<p class="graf graf--p graf-after--p" id="416b" name="416b">
[http://makeyourownneuralnetwork.blogspot.com/2016/06/bias-nodes-in-neural-networks.html Bias Nodes in Neural Networks]
(makeyourownneuralnetwork.blogspot.com)
</p>
<p class="graf graf--p graf-after--p" id="1e2e" name="1e2e">
[https://www.quora.com/What-is-bias-in-artificial-neural-network What is bias in artificial neural network?]
(quora.com)
</p>
<h4 class="graf graf--h4 graf-after--p" id="b121" name="b121">
Perceptron
</h4>
<p class="graf graf--p graf-after--h4" id="b606" name="b606">
[http://neuralnetworksanddeeplearning.com/chap1.html#perceptrons Perceptrons]
(neuralnetworksanddeeplearning.com)
</p>
<p class="graf graf--p graf-after--p" id="5ad3" name="5ad3">
[http://natureofcode.com/book/chapter-10-neural-networks/#chapter10_figure3 The Perception]
(natureofcode.com)
</p>
<p class="graf graf--p graf-after--p" id="f687" name="f687">
[http://computing.dcu.ie/~humphrys/Notes/Neural/single.neural.html Single-layer Neural Networks (Perceptrons)]
(dcu.ie)
</p>
<p class="graf graf--p graf-after--p" id="9be4" name="9be4">
[https://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks From Perceptrons to Deep Networks]
(toptal.com)
</p>
<h4 class="graf graf--h4 graf-after--p" id="7361" name="7361">
Regression
</h4>
<p class="graf graf--p graf-after--h4" id="ee28" name="ee28">
[http://people.duke.edu/~rnau/regintro.htm Introduction to linear regression analysis]
(duke.edu)
</p>
<p class="graf graf--p graf-after--p" id="fdcf" name="fdcf">
[http://ufldl.stanford.edu/tutorial/supervised/LinearRegression/ Linear Regression]
(ufldl.stanford.edu)
</p>
<p class="graf graf--p graf-after--p" id="b3c3" name="b3c3">
[http://ml-cheatsheet.readthedocs.io/en/latest/linear_regression.html Linear Regression]
(readthedocs.io)
</p>
<p class="graf graf--p graf-after--p" id="f22e" name="f22e">
[http://ml-cheatsheet.readthedocs.io/en/latest/logistic_regression.html Logistic Regression]
(readthedocs.io)
</p>
<p class="graf graf--p graf-after--p" id="bf4c" name="bf4c">
[http://machinelearningmastery.com/simple-linear-regression-tutorial-for-machine-learning/ Simple Linear Regression Tutorial for Machine Learning]
(machinelearningmastery.com)
</p>
<p class="graf graf--p graf-after--p" id="3b80" name="3b80">
[http://machinelearningmastery.com/logistic-regression-tutorial-for-machine-learning/ Logistic Regression Tutorial for Machine Learning]
(machinelearningmastery.com)
</p>
<p class="graf graf--p graf-after--p" id="e2da" name="e2da">
[http://ufldl.stanford.edu/tutorial/supervised/SoftmaxRegression/ Softmax Regression]
(ufldl.stanford.edu)
</p>
<h4 class="graf graf--h4 graf-after--p" id="a626" name="a626">
Gradient Descent
</h4>
<p class="graf graf--p graf-after--h4" id="3992" name="3992">
[http://neuralnetworksanddeeplearning.com/chap1.html#learning_with_gradient_descent Learning with gradient descent]
(neuralnetworksanddeeplearning.com)
</p>
<p class="graf graf--p graf-after--p" id="671e" name="671e">
[http://iamtrask.github.io/2015/07/27/python-network-part2/ Gradient Descent]
(iamtrask.github.io)
</p>
<p class="graf graf--p graf-after--p" id="46fe" name="46fe">
[http://www.kdnuggets.com/2017/04/simple-understand-gradient-descent-algorithm.html How to understand Gradient Descent algorithm]
(kdnuggets.com)
</p>
<p class="graf graf--p graf-after--p" id="97ef" name="97ef">
[http://sebastianruder.com/optimizing-gradient-descent/ An overview of gradient descent optimization algorithms]
(sebastianruder.com)
</p>
<p class="graf graf--p graf-after--p" id="f482" name="f482">
[http://cs231n.github.io/optimization-1/ Optimization: Stochastic Gradient Descent]
(Stanford CS231n)
</p>
<h4 class="graf graf--h4 graf-after--p" id="e465" name="e465">
Generative Learning
</h4>
<p class="graf graf--p graf-after--h4" id="1436" name="1436">
[http://cs229.stanford.edu/notes/cs229-notes2.pdf Generative Learning Algorithms] [[File:cs229-notes2_4278.pdf]]
(Stanford CS229)
</p>
<p class="graf graf--p graf-after--p" id="06d6" name="06d6">
[https://monkeylearn.com/blog/practical-explanation-naive-bayes-classifier/ A practical explanation of a Naive Bayes classifier]
(monkeylearn.com)
</p>
<h4 class="graf graf--h4 graf-after--p" id="0d39" name="0d39">
Support Vector Machines
</h4>
<p class="graf graf--p graf-after--h4" id="0a50" name="0a50">
[https://monkeylearn.com/blog/introduction-to-support-vector-machines-svm/ An introduction to Support Vector Machines (SVM)]
(monkeylearn.com)
</p>
<p class="graf graf--p graf-after--p" id="521a" name="521a">
[http://cs229.stanford.edu/notes/cs229-notes3.pdf Support Vector Machines] [[File:cs229-notes3_1513.pdf]]
(Stanford CS229)
</p>
<p class="graf graf--p graf-after--p" id="784a" name="784a">
[http://cs231n.github.io/linear-classify/ Linear classification: Support Vector Machine, Softmax]
(Stanford 231n)
</p>
<h4 class="graf graf--h4 graf-after--p" id="3fe9" name="3fe9">
Backpropagation
</h4>
<p class="graf graf--p graf-after--h4" id="cbbc" name="cbbc">
[https://medium.com/@karpathy/yes-you-should-understand-backprop-e2f06eab496b Yes you should understand backprop]
(medium.com/@karpathy)
</p>
<p class="graf graf--p graf-after--p" id="1916" name="1916">
[https://github.com/rasbt/python-machine-learning-book/blob/master/faq/visual-backpropagation.md Can you give a visual explanation for the back propagation algorithm for neural networks?]
(github.com/rasbt)
</p>
<p class="graf graf--p graf-after--p" id="8e33" name="8e33">
[http://neuralnetworksanddeeplearning.com/chap2.html How the backpropagation algorithm works]
(neuralnetworksanddeeplearning.com)
</p>
<p class="graf graf--p graf-after--p" id="8e19" name="8e19">
[http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/ Backpropagation Through Time and Vanishing Gradients]
(wildml.com)
</p>
<p class="graf graf--p graf-after--p" id="8642" name="8642">
[http://machinelearningmastery.com/gentle-introduction-backpropagation-time/ A Gentle Introduction to Backpropagation Through Time]
(machinelearningmastery.com)
</p>
<p class="graf graf--p graf-after--p" id="e081" name="e081">
[http://cs231n.github.io/optimization-2/ Backpropagation, Intuitions]
(Stanford CS231n)
</p>
<h4 class="graf graf--h4 graf-after--p" id="f74b" name="f74b">
Deep Learning
</h4>
<p class="graf graf--p graf-after--h4" id="d32f" name="d32f">
[http://nikhilbuduma.com/2014/12/29/deep-learning-in-a-nutshell/ Deep Learning in a Nutshell]
(nikhilbuduma.com)
</p>
<p class="graf graf--p graf-after--p" id="45bd" name="45bd">
[http://ai.stanford.edu/~quocle/tutorial1.pdf A Tutorial on Deep Learning] [[File:tutorial1_8291.pdf]]
(Quoc V. Le)
</p>
<p class="graf graf--p graf-after--p" id="2b94" name="2b94">
[http://machinelearningmastery.com/what-is-deep-learning/ What is Deep Learning?]
(machinelearningmastery.com)
</p>
<p class="graf graf--p graf-after--p" id="3a94" name="3a94">
[https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/ What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning?]
(nvidia.com)
</p>
<h4 class="graf graf--h4 graf-after--p" id="d882" name="d882">
Optimization and Dimensionality Reduction
</h4>
<p class="graf graf--p graf-after--h4" id="f285" name="f285">
[https://www.knime.org/blog/seven-techniques-for-data-dimensionality-reduction Seven Techniques for Data Dimensionality Reduction]
(knime.org)
</p>
<p class="graf graf--p graf-after--p" id="551b" name="551b">
[http://cs229.stanford.edu/notes/cs229-notes10.pdf Principal components analysis] [[File:cs229-notes10_9361.pdf]]
(Stanford CS229)
</p>
<p class="graf graf--p graf-after--p" id="1d6d" name="1d6d">
[http://videolectures.net/site/normal_dl/tag=741100/nips2012_hinton_networks_01.pdf Dropout: A simple way to improve neural networks] [[File:nips2012_hinton_networks_01_4884.pdf]]
(Hinton @ NIPS 2012)
</p>
<p class="graf graf--p graf-after--p" id="0276" name="0276">
[http://rishy.github.io/ml/2017/01/05/how-to-train-your-dnn/ How to train your Deep Neural Network]
(rishy.github.io)
</p>
<h4 class="graf graf--h4 graf-after--p" id="c977" name="c977">
Long Short Term Memory (LSTM)
</h4>
<p class="graf graf--p graf-after--h4" id="284f" name="284f">
[http://machinelearningmastery.com/gentle-introduction-long-short-term-memory-networks-experts/ A Gentle Introduction to Long Short-Term Memory Networks by the Experts]
(machinelearningmastery.com)
</p>
<p class="graf graf--p graf-after--p" id="b63d" name="b63d">
[http://colah.github.io/posts/2015-08-Understanding-LSTMs/ Understanding LSTM Networks]
(colah.github.io)
</p>
<p class="graf graf--p graf-after--p" id="7b7f" name="7b7f">
[http://blog.echen.me/2017/05/30/exploring-lstms/ Exploring LSTMs]
(echen.me)
</p>
<p class="graf graf--p graf-after--p" id="394d" name="394d">
[http://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/ Anyone Can Learn To Code an LSTM-RNN in Python]
(iamtrask.github.io)
</p>
<h4 class="graf graf--h4 graf-after--p" id="4020" name="4020">
Convolutional Neural Networks (CNNs)
</h4>
<p class="graf graf--p graf-after--h4" id="11a8" name="11a8">
[http://neuralnetworksanddeeplearning.com/chap6.html#introducing_convolutional_networks Introducing convolutional networks]
(neuralnetworksanddeeplearning.com)
</p>
<p class="graf graf--p graf-after--p" id="4578" name="4578">
[https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721 Deep Learning and Convolutional Neural Networks]
(medium.com/@ageitgey)
</p>
<p class="graf graf--p graf-after--p" id="b8c8" name="b8c8">
[http://colah.github.io/posts/2014-07-Conv-Nets-Modular/ Conv Nets: A Modular Perspective]
(colah.github.io)
</p>
<p class="graf graf--p graf-after--p" id="0492" name="0492">
[http://colah.github.io/posts/2014-07-Understanding-Convolutions/ Understanding Convolutions]
(colah.github.io)
</p>
<h4 class="graf graf--h4 graf-after--p" id="6f68" name="6f68">
Recurrent Neural Nets (RNNs)
</h4>
<p class="graf graf--p graf-after--h4" id="15d8" name="15d8">
[http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/ Recurrent Neural Networks Tutorial]
(wildml.com)
</p>
<p class="graf graf--p graf-after--p" id="5032" name="5032">
[http://distill.pub/2016/augmented-rnns/ Attention and Augmented Recurrent Neural Networks]
(distill.pub)
</p>
<p class="graf graf--p graf-after--p" id="7e49" name="7e49">
[http://karpathy.github.io/2015/05/21/rnn-effectiveness/ The Unreasonable Effectiveness of Recurrent Neural Networks]
(karpathy.github.io)
</p>
<p class="graf graf--p graf-after--p" id="20ea" name="20ea">
[http://nikhilbuduma.com/2015/01/11/a-deep-dive-into-recurrent-neural-networks/ A Deep Dive into Recurrent Neural Nets]
(nikhilbuduma.com)
</p>
<h4 class="graf graf--h4 graf-after--p" id="a92e" name="a92e">
Reinforcement Learning
</h4>
<p class="graf graf--p graf-after--h4" id="9c82" name="9c82">
[https://www.analyticsvidhya.com/blog/2017/01/introduction-to-reinforcement-learning-implementation/ Simple Beginner’s guide to Reinforcement Learning & its implementation]
(analyticsvidhya.com)
</p>
<p class="graf graf--p graf-after--p" id="b4e6" name="b4e6">
[https://web.mst.edu/~gosavia/tutorial.pdf A Tutorial for Reinforcement Learning] [[File:tutorial_5972.pdf]]
(mst.edu)
</p>
<p class="graf graf--p graf-after--p" id="8c06" name="8c06">
[http://www.wildml.com/2016/10/learning-reinforcement-learning/ Learning Reinforcement Learning]
(wildml.com)
</p>
<p class="graf graf--p graf-after--p" id="a8c2" name="a8c2">
[http://karpathy.github.io/2016/05/31/rl/ Deep Reinforcement Learning: Pong from Pixels]
(karpathy.github.io)
</p>
<h4 class="graf graf--h4 graf-after--p" id="e63f" name="e63f">
Generative Adversarial Networks (GANs)
</h4>
<p class="graf graf--p graf-after--h4" id="a3c3" name="a3c3">
[https://blogs.nvidia.com/blog/2017/05/17/generative-adversarial-network/ What’s a Generative Adversarial Network?]
(nvidia.com)
</p>
<p class="graf graf--p graf-after--p" id="684f" name="684f">
[https://medium.com/@ageitgey/abusing-generative-adversarial-networks-to-make-8-bit-pixel-art-e45d9b96cee7 Abusing Generative Adversarial Networks to Make 8-bit Pixel Art]
(medium.com/@ageitgey)
</p>
<p class="graf graf--p graf-after--p" id="cfef" name="cfef">
[http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/ An introduction to Generative Adversarial Networks (with code in TensorFlow)]
(aylien.com)
</p>
<p class="graf graf--p graf-after--p" id="16fd" name="16fd">
[https://www.oreilly.com/learning/generative-adversarial-networks-for-beginners Generative Adversarial Networks for Beginners]
(oreilly.com)
</p>
<h4 class="graf graf--h4 graf-after--p" id="19ff" name="19ff">
Multi-task Learning
</h4>
<p class="graf graf--p graf-after--h4" id="8292" name="8292">
[http://sebastianruder.com/multi-task/index.html An Overview of Multi-Task Learning in Deep Neural Networks]
(sebastianruder.com)
</p>
<h3 class="graf graf--h3 graf-after--p" id="2c96" name="2c96">
NLP
</h3>
<p class="graf graf--p graf-after--h3" id="6158" name="6158">
[http://u.cs.biu.ac.il/~yogo/nnlp.pdf A Primer on Neural Network Models for Natural Language Processing] [[File:nnlp_8731.pdf]]
(Yoav Goldberg)
</p>
<p class="graf graf--p graf-after--p" id="d063" name="d063">
[https://monkeylearn.com/blog/the-definitive-guide-to-natural-language-processing/ The Definitive Guide to Natural Language Processing]
(monkeylearn.com)
</p>
<p class="graf graf--p graf-after--p" id="b2fc" name="b2fc">
[https://blog.algorithmia.com/introduction-natural-language-processing-nlp/ Introduction to Natural Language Processing]
(algorithmia.com)
</p>
<p class="graf graf--p graf-after--p" id="d6ba" name="d6ba">
[http://www.vikparuchuri.com/blog/natural-language-processing-tutorial/ Natural Language Processing Tutorial]
(vikparuchuri.com)
</p>
<p class="graf graf--p graf-after--p" id="1d24" name="1d24">
[https://arxiv.org/pdf/1103.0398.pdf Natural Language Processing (almost) from Scratch] [[File:1103.0398_7586.pdf]]
(arxiv.org)
</p>
<h4 class="graf graf--h4 graf-after--p" id="53ca" name="53ca">
Deep Learning and NLP
</h4>
<p class="graf graf--p graf-after--h4" id="39ff" name="39ff">
[https://arxiv.org/pdf/1703.03091.pdf Deep Learning applied to NLP] [[File:1703.03091_6228.pdf]]
(arxiv.org)
</p>
<p class="graf graf--p graf-after--p" id="c060" name="c060">
[https://nlp.stanford.edu/courses/NAACL2013/NAACL2013-Socher-Manning-DeepLearning.pdf Deep Learning for NLP (without Magic)] [[File:NAACL2013-Socher-Manning-DeepLearning_4422.pdf]]
(Richard Socher)
</p>
<p class="graf graf--p graf-after--p" id="c54f" name="c54f">
[http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/ Understanding Convolutional Neural Networks for NLP]
(wildml.com)
</p>
<p class="graf graf--p graf-after--p" id="7cc3" name="7cc3">
[http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/ Deep Learning, NLP, and Representations]
(colah.github.io)
</p>
<p class="graf graf--p graf-after--p" id="d7ff" name="d7ff">
[https://explosion.ai/blog/deep-learning-formula-nlp Embed, encode, attend, predict: The new deep learning formula for state-of-the-art NLP models]
(explosion.ai)
</p>
<p class="graf graf--p graf-after--p" id="9297" name="9297">
[https://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/ Understanding Natural Language with Deep Neural Networks Using Torch]
(nvidia.com)
</p>
<p class="graf graf--p graf-after--p" id="acf2" name="acf2">
[http://pytorch.org/tutorials/beginner/deep_learning_nlp_tutorial.html Deep Learning for NLP with Pytorch]
(pytorich.org)
</p>
<h4 class="graf graf--h4 graf-after--p" id="3af7" name="3af7">
Word Vectors
</h4>
<p class="graf graf--p graf-after--h4" id="4174" name="4174">
[https://www.kaggle.com/c/word2vec-nlp-tutorial Bag of Words Meets Bags of Popcorn]
(kaggle.com)
</p>
<p class="graf graf--p graf-after--p" id="5e59" name="5e59">
On word embeddings
[http://sebastianruder.com/word-embeddings-1/index.html Part I]
,
[http://sebastianruder.com/word-embeddings-softmax/index.html Part II]
,
[http://sebastianruder.com/secret-word2vec/index.html Part III]
(sebastianruder.com)
</p>
<p class="graf graf--p graf-after--p" id="9cee" name="9cee">
[https://blog.acolyer.org/2016/04/21/the-amazing-power-of-word-vectors/ The amazing power of word vectors]
(acolyer.org)
</p>
<p class="graf graf--p graf-after--p" id="184d" name="184d">
[https://arxiv.org/pdf/1411.2738.pdf word2vec Parameter Learning Explained] [[File:1411.2738_1549.pdf]]
(arxiv.org)
</p>
<p class="graf graf--p graf-after--p" id="8ab5" name="8ab5">
Word2Vec Tutorial —
[http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/ The Skip-Gram Model]
,
[http://mccormickml.com/2017/01/11/word2vec-tutorial-part-2-negative-sampling/ Negative Sampling]
(mccormickml.com)
</p>
<h4 class="graf graf--h4 graf-after--p" id="0138" name="0138">
Encoder-Decoder
</h4>
<p class="graf graf--p graf-after--h4" id="6b05" name="6b05">
[http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/ Attention and Memory in Deep Learning and NLP]
(wildml.com)
</p>
<p class="graf graf--p graf-after--p" id="2fe7" name="2fe7">
[https://www.tensorflow.org/tutorials/seq2seq Sequence to Sequence Models]
(tensorflow.org)
</p>
<p class="graf graf--p graf-after--p" id="1b30" name="1b30">
[https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Sequence to Sequence Learning with Neural Networks] [[File:5346-sequence-to-sequence-learning-with-neural-networks_2629.pdf]]
(NIPS 2014)
</p>
<p class="graf graf--p graf-after--p" id="c9a6" name="c9a6">
[https://medium.com/@ageitgey/machine-learning-is-fun-part-5-language-translation-with-deep-learning-and-the-magic-of-sequences-2ace0acca0aa Machine Learning is Fun Part 5: Language Translation with Deep Learning and the Magic of Sequences]
(medium.com/@ageitgey)
</p>
<p class="graf graf--p graf-after--p" id="9f8c" name="9f8c">
[http://machinelearningmastery.com/how-to-use-an-encoder-decoder-lstm-to-echo-sequences-of-random-integers/ How to use an Encoder-Decoder LSTM to Echo Sequences of Random Integers]
(machinelearningmastery.com)
</p>
<p class="graf graf--p graf-after--p" id="ca31" name="ca31">
[https://google.github.io/seq2seq/ tf-seq2seq]
(google.github.io)
</p>
<h3 class="graf graf--h3 graf-after--p" id="a456" name="a456">
Python
</h3>
<p class="graf graf--p graf-after--h3" id="41bc" name="41bc">
[http://www.kdnuggets.com/2015/11/seven-steps-machine-learning-python.html 7 Steps to Mastering Machine Learning With Python]
(kdnuggets.com)
</p>
<p class="graf graf--p graf-after--p" id="a52f" name="a52f">
[http://nbviewer.jupyter.org/github/rhiever/Data-Analysis-and-Machine-Learning-Projects/blob/master/example-data-science-notebook/Example%20Machine%20Learning%20Notebook.ipynb An example machine learning notebook]
(nbviewer.jupyter.org)
</p>
<h4 class="graf graf--h4 graf-after--p" id="642b" name="642b">
Examples
</h4>
<p class="graf graf--p graf-after--h4" id="fe69" name="fe69">
[http://machinelearningmastery.com/implement-perceptron-algorithm-scratch-python/ How To Implement The Perceptron Algorithm From Scratch In Python]
(machinelearningmastery.com)
</p>
<p class="graf graf--p graf-after--p" id="8b96" name="8b96">
[http://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/ Implementing a Neural Network from Scratch in Python]
(wildml.com)
</p>
<p class="graf graf--p graf-after--p" id="7ab0" name="7ab0">
[http://iamtrask.github.io/2015/07/12/basic-python-network/ A Neural Network in 11 lines of Python]
(iamtrask.github.io)
</p>
<p class="graf graf--p graf-after--p" id="7770" name="7770">
[http://www.kdnuggets.com/2016/01/implementing-your-own-knn-using-python.html Implementing Your Own k-Nearest Neighbour Algorithm Using Python]
(kdnuggets.com)
</p>
<p class="graf graf--p graf-after--p" id="ec81" name="ec81">
[http://machinelearningmastery.com/memory-in-a-long-short-term-memory-network/ Demonstration of Memory with a Long Short-Term Memory Network in Python]
(machinelearningmastery.com)
</p>
<p class="graf graf--p graf-after--p" id="9858" name="9858">
[http://machinelearningmastery.com/learn-echo-random-integers-long-short-term-memory-recurrent-neural-networks/ How to Learn to Echo Random Integers with Long Short-Term Memory Recurrent Neural Networks]
(machinelearningmastery.com)
</p>
<p class="graf graf--p graf-after--p" id="9b28" name="9b28">
[http://machinelearningmastery.com/learn-add-numbers-seq2seq-recurrent-neural-networks/ How to Learn to Add Numbers with seq2seq Recurrent Neural Networks]
(machinelearningmastery.com)
</p>
<h4 class="graf graf--h4 graf-after--p" id="347e" name="347e">
Scipy and numpy
</h4>
<p class="graf graf--p graf-after--h4" id="4b1e" name="4b1e">
[http://www.scipy-lectures.org/ Scipy Lecture Notes]
(scipy-lectures.org)
</p>
<p class="graf graf--p graf-after--p" id="d726" name="d726">
[http://cs231n.github.io/python-numpy-tutorial/ Python Numpy Tutorial]
(Stanford CS231n)
</p>
<p class="graf graf--p graf-after--p" id="afb0" name="afb0">
[https://engineering.ucsb.edu/~shell/che210d/numpy.pdf An introduction to Numpy and Scipy] [[File:numpy_7659.pdf]]
(UCSB CHE210D)
</p>
<p class="graf graf--p graf-after--p" id="7b3a" name="7b3a">
[http://nbviewer.jupyter.org/gist/rpmuller/5920182#ii.-numpy-and-scipy A Crash Course in Python for Scientists]
(nbviewer.jupyter.org)
</p>
<h4 class="graf graf--h4 graf-after--p" id="34ec" name="34ec">
scikit-learn
</h4>
<p class="graf graf--p graf-after--h4" id="9278" name="9278">
[http://nbviewer.jupyter.org/github/jakevdp/sklearn_pycon2015/blob/master/notebooks/Index.ipynb PyCon scikit-learn Tutorial Index]
(nbviewer.jupyter.org)
</p>
<p class="graf graf--p graf-after--p" id="0c41" name="0c41">
[https://github.com/mmmayo13/scikit-learn-classifiers/blob/master/sklearn-classifiers-tutorial.ipynb scikit-learn Classification Algorithms]
(github.com/mmmayo13)
</p>
<p class="graf graf--p graf-after--p" id="fbd9" name="fbd9">
[http://scikit-learn.org/stable/tutorial/index.html scikit-learn Tutorials]
(scikit-learn.org)
</p>
<p class="graf graf--p graf-after--p" id="0f66" name="0f66">
[https://github.com/mmmayo13/scikit-learn-beginners-tutorials Abridged scikit-learn Tutorials]
(github.com/mmmayo13)
</p>
<h4 class="graf graf--h4 graf-after--p" id="a1b6" name="a1b6">
Tensorflow
</h4>
<p class="graf graf--p graf-after--h4" id="921c" name="921c">
[https://www.tensorflow.org/tutorials/ Tensorflow Tutorials]
(tensorflow.org)
</p>
<p class="graf graf--p graf-after--p" id="818f" name="818f">
[https://medium.com/@erikhallstrm/hello-world-tensorflow-649b15aed18c Introduction to TensorFlow — CPU vs GPU]
(medium.com/@erikhallstrm)
</p>
<p class="graf graf--p graf-after--p" id="5cae" name="5cae">
[https://blog.metaflow.fr/tensorflow-a-primer-4b3fa0978be3 TensorFlow: A primer]
(metaflow.fr)
</p>
<p class="graf graf--p graf-after--p" id="0e5f" name="0e5f">
[http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/ RNNs in Tensorflow]
(wildml.com)
</p>
<p class="graf graf--p graf-after--p" id="6b85" name="6b85">
[http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/ Implementing a CNN for Text Classification in TensorFlow]
(wildml.com)
</p>
<p class="graf graf--p graf-after--p" id="4f3b" name="4f3b">
[http://pavel.surmenok.com/2016/10/15/how-to-run-text-summarization-with-tensorflow/ How to Run Text Summarization with TensorFlow]
(surmenok.com)
</p>
<h4 class="graf graf--h4 graf-after--p" id="03ef" name="03ef">
PyTorch
</h4>
<p class="graf graf--p graf-after--h4" id="76c7" name="76c7">
[http://pytorch.org/tutorials/ PyTorch Tutorials]
(pytorch.org)
</p>
<p class="graf graf--p graf-after--p" id="b62c" name="b62c">
[http://blog.gaurav.im/2017/04/24/a-gentle-intro-to-pytorch/ A Gentle Intro to PyTorch]
(gaurav.im)
</p>
<p class="graf graf--p graf-after--p" id="9db7" name="9db7">
[https://iamtrask.github.io/2017/01/15/pytorch-tutorial/ Tutorial: Deep Learning in PyTorch]
(iamtrask.github.io)
</p>
<p class="graf graf--p graf-after--p" id="9f68" name="9f68">
[https://github.com/jcjohnson/pytorch-examples PyTorch Examples]
(github.com/jcjohnson)
</p>
<p class="graf graf--p graf-after--p" id="c997" name="c997">
[https://github.com/MorvanZhou/PyTorch-Tutorial PyTorch Tutorial]
(github.com/MorvanZhou)
</p>
<p class="graf graf--p graf-after--p" id="c168" name="c168">
[https://github.com/yunjey/pytorch-tutorial PyTorch Tutorial for Deep Learning Researchers]
(github.com/yunjey)
</p>
<h3 class="graf graf--h3 graf-after--p" id="8244" name="8244">
Math
</h3>
<p class="graf graf--p graf-after--h3" id="2686" name="2686">
[https://people.ucsc.edu/~praman1/static/pub/math-for-ml.pdf Math for Machine Learning] [[File:math-for-ml_251.pdf]]
(ucsc.edu)
</p>
<p class="graf graf--p graf-after--p" id="d01e" name="d01e">
[http://www.umiacs.umd.edu/~hal/courses/2013S_ML/math4ml.pdf Math for Machine Learning] [[File:math4ml_2556.pdf]]
(UMIACS CMSC422)
</p>
<h4 class="graf graf--h4 graf-after--p" id="492a" name="492a">
Linear algebra
</h4>
<p class="graf graf--p graf-after--h4" id="6ca6" name="6ca6">
[https://betterexplained.com/articles/linear-algebra-guide/ An Intuitive Guide to Linear Algebra]
(betterexplained.com)
</p>
<p class="graf graf--p graf-after--p" id="e264" name="e264">
[https://betterexplained.com/articles/matrix-multiplication/ A Programmer’s Intuition for Matrix Multiplication]
(betterexplained.com)
</p>
<p class="graf graf--p graf-after--p" id="aa01" name="aa01">
[https://betterexplained.com/articles/cross-product/ Understanding the Cross Product]
(betterexplained.com)
</p>
<p class="graf graf--p graf-after--p" id="3135" name="3135">
[https://betterexplained.com/articles/vector-calculus-understanding-the-dot-product/ Understanding the Dot Product]
(betterexplained.com)
</p>
<p class="graf graf--p graf-after--p" id="07be" name="07be">
[http://www.cedar.buffalo.edu/~srihari/CSE574/Chap1/LinearAlgebra.pdf Linear Algebra for Machine Learning] [[File:LinearAlgebra_7857.pdf]]
(U. of Buffalo CSE574)
</p>
<p class="graf graf--p graf-after--p" id="c447" name="c447">
[https://medium.com/towards-data-science/linear-algebra-cheat-sheet-for-deep-learning-cd67aba4526c Linear algebra cheat sheet for deep learning]
(medium.com)
</p>
<p class="graf graf--p graf-after--p" id="eca5" name="eca5">
[http://cs229.stanford.edu/section/cs229-linalg.pdf Linear Algebra Review and Reference] [[File:cs229-linalg_9803.pdf]]
(Stanford CS229)
</p>
<h4 class="graf graf--h4 graf-after--p" id="3209" name="3209">
Probability
</h4>
<p class="graf graf--p graf-after--h4" id="92fb" name="92fb">
[https://betterexplained.com/articles/understanding-bayes-theorem-with-ratios/ Understanding Bayes Theorem With Ratios]
(betterexplained.com)
</p>
<p class="graf graf--p graf-after--p" id="37f4" name="37f4">
[http://cs229.stanford.edu/section/cs229-prob.pdf Review of Probability Theory] [[File:cs229-prob_3688.pdf]]
(Stanford CS229)
</p>
<p class="graf graf--p graf-after--p" id="71b3" name="71b3">
[https://see.stanford.edu/materials/aimlcs229/cs229-prob.pdf Probability Theory Review for Machine Learning] [[File:cs229-prob_833.pdf]]
(Stanford CS229)
</p>
<p class="graf graf--p graf-after--p" id="5de1" name="5de1">
[http://www.cedar.buffalo.edu/~srihari/CSE574/Chap1/Probability-Theory.pdf Probability Theory] [[File:Probability-Theory_1042.pdf]]
(U. of Buffalo CSE574)
</p>
<p class="graf graf--p graf-after--p" id="053f" name="053f">
[http://www.cs.toronto.edu/~urtasun/courses/CSC411_Fall16/tutorial1.pdf Probability Theory for Machine Learning] [[File:tutorial1_4000.pdf]]
(U. of Toronto CSC411)
</p>
<h4 class="graf graf--h4 graf-after--p" id="a689" name="a689">
Calculus
</h4>
<p class="graf graf--p graf-after--h4" id="feb1" name="feb1">
[https://betterexplained.com/articles/how-to-understand-derivatives-the-quotient-rule-exponents-and-logarithms/ How To Understand Derivatives: The Quotient Rule, Exponents, and Logarithms]
(betterexplained.com)
</p>
<p class="graf graf--p graf-after--p" id="1df5" name="1df5">
[https://betterexplained.com/articles/derivatives-product-power-chain/ How To Understand Derivatives: The Product, Power & Chain Rules]
(betterexplained.com)
</p>
<p class="graf graf--p graf-after--p" id="4f4d" name="4f4d">
[https://betterexplained.com/articles/vector-calculus-understanding-the-gradient/ Vector Calculus: Understanding the Gradient]
(betterexplained.com)
</p>
<p class="graf graf--p graf-after--p" id="151c" name="151c">
[http://web.stanford.edu/class/cs224n/lecture_notes/cs224n-2017-review-differential-calculus.pdf Differential Calculus] [[File:cs224n-2017-review-differential-calculus_4842.pdf]]
(Stanford CS224n)
</p>
<p class="graf graf--p graf-after--p graf--trailing" id="0ffd" name="0ffd">
[http://ml-cheatsheet.readthedocs.io/en/latest/calculus.html Calculus Overview]
(readthedocs.io)
</p>
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