August 20, 2018 By , 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. tutorial I’ve split this post into four sections: , , , and . 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. Machine Learning NLP Python Math If there are 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 a 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. (Source via — Robbie Allen) good Machine Learning (medium.com/@ageitgey) Machine Learning is Fun! Machine Learning Crash Course: , , (Machine Learning at Berkeley) Part I Part II Part III (toptal.com) An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples (monkeylearn.com) A Gentle Guide to Machine Learning (sas.com) Which machine learning algorithm should I use? Activation and Loss Functions (neuralnetworksanddeeplearning.com) Sigmoid neurons (quora.com) What is the role of the activation function in a neural network? (stats.stackexchange.com) Comprehensive list of activation functions in neural networks with pros/cons (medium.com) Activation functions and it’s types-Which is better? (exegetic.biz) Making Sense of Logarithmic Loss (Stanford CS231n) Loss Functions (rishy.github.io) L1 vs. L2 Loss function (neuralnetworksanddeeplearning.com) The cross-entropy cost function Bias (stackoverflow.com) Role of Bias in Neural Networks (makeyourownneuralnetwork.blogspot.com) Bias Nodes in Neural Networks (quora.com) What is bias in artificial neural network? Perceptron (neuralnetworksanddeeplearning.com) Perceptrons (natureofcode.com) The Perception (dcu.ie) Single-layer Neural Networks (Perceptrons) (toptal.com) From Perceptrons to Deep Networks Regression (duke.edu) Introduction to linear regression analysis (ufldl.stanford.edu) Linear Regression (readthedocs.io) Linear Regression (readthedocs.io) Logistic Regression (machinelearningmastery.com) Simple Linear Regression Tutorial for Machine Learning (machinelearningmastery.com) Logistic Regression Tutorial for Machine Learning (ufldl.stanford.edu) Softmax Regression Gradient Descent (neuralnetworksanddeeplearning.com) Learning with gradient descent (iamtrask.github.io) Gradient Descent (kdnuggets.com) How to understand Gradient Descent algorithm (sebastianruder.com) An overview of gradient descent optimization algorithms (Stanford CS231n) Optimization: Stochastic Gradient Descent Generative Learning (Stanford CS229) Generative Learning Algorithms (monkeylearn.com) A practical explanation of a Naive Bayes classifier Support Vector Machines (monkeylearn.com) An introduction to Support Vector Machines (SVM) (Stanford CS229) Support Vector Machines (Stanford 231n) Linear classification: Support Vector Machine, Softmax Backpropagation (medium.com/@karpathy) Yes you should understand backprop (github.com/rasbt) Can you give a visual explanation for the back propagation algorithm for neural networks? (neuralnetworksanddeeplearning.com) How the backpropagation algorithm works (wildml.com) Backpropagation Through Time and Vanishing Gradients (machinelearningmastery.com) A Gentle Introduction to Backpropagation Through Time (Stanford CS231n) Backpropagation, Intuitions Deep Learning (nikhilbuduma.com) Deep Learning in a Nutshell (Quoc V. Le) A Tutorial on Deep Learning (machinelearningmastery.com) What is Deep Learning? (nvidia.com) What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning? Optimization and Dimensionality Reduction (knime.org) Seven Techniques for Data Dimensionality Reduction (Stanford CS229) Principal components analysis (Hinton @ NIPS 2012) Dropout: A simple way to improve neural networks (rishy.github.io) How to train your Deep Neural Network Long Short-Term Memory (LSTM) (machinelearningmastery.com) A Gentle Introduction to Long Short-Term Memory Networks by the Experts (colah.github.io) Understanding LSTM Networks (echen.me) Exploring LSTMs (iamtrask.github.io) Anyone Can Learn To Code an LSTM-RNN in Python Convolutional Neural Networks (CNNs) (neuralnetworksanddeeplearning.com) Introducing convolutional networks (medium.com/@ageitgey) Deep Learning and Convolutional Neural Networks (colah.github.io) Conv Nets: A Modular Perspective (colah.github.io) Understanding Convolutions Recurrent Neural Nets (RNNs) (wildml.com) Recurrent Neural Networks Tutorial (distill.pub) Attention and Augmented Recurrent Neural Networks (karpathy.github.io) The Unreasonable Effectiveness of Recurrent Neural Networks (nikhilbuduma.com) A Deep Dive into Recurrent Neural Nets Reinforcement Learning (analyticsvidhya.com) Simple Beginner’s guide to Reinforcement Learning & its implementation (mst.edu) A Tutorial for Reinforcement Learning (wildml.com) Learning Reinforcement Learning (karpathy.github.io) Deep Reinforcement Learning: Pong from Pixels Generative Adversarial Networks (GANs) (nvidia.com) What’s a Generative Adversarial Network? (medium.com/@ageitgey) Abusing Generative Adversarial Networks to Make 8-bit Pixel Art (aylien.com) An introduction to Generative Adversarial Networks (with code in TensorFlow) (oreilly.com) Generative Adversarial Networks for Beginners Multi-task Learning (sebastianruder.com) An Overview of Multi-Task Learning in Deep Neural Networks NLP (Yoav Goldberg) A Primer on Neural Network Models for Natural Language Processing (monkeylearn.com) The Definitive Guide to Natural Language Processing (algorithmia.com) Introduction to Natural Language Processing (vikparuchuri.com) Natural Language Processing Tutorial (arxiv.org) Natural Language Processing (almost) from Scratch Deep Learning and NLP (arxiv.org) Deep Learning applied to NLP (Richard Socher) Deep Learning for NLP (without Magic) (wildml.com) Understanding Convolutional Neural Networks for NLP (colah.github.io) Deep Learning, NLP, and Representations (explosion.ai) Embed, encode, attend, predict: The new deep learning formula for state-of-the-art NLP models (nvidia.com) Understanding Natural Language with Deep Neural Networks Using Torch (pytorich.org) Deep Learning for NLP with Pytorch Word Vectors (kaggle.com) Bag of Words Meets Bags of Popcorn On word embeddings , , (sebastianruder.com) Part I Part II Part III (acolyer.org) The amazing power of word vectors (arxiv.org) word2vec Parameter Learning Explained Word2Vec Tutorial — , (mccormickml.com) The Skip-Gram Model Negative Sampling Encoder-Decoder (wildml.com) Attention and Memory in Deep Learning and NLP (tensorflow.org) Sequence to Sequence Models (NIPS 2014) Sequence to Sequence Learning with Neural Networks (medium.com/@ageitgey) Machine Learning is Fun Part 5: Language Translation with Deep Learning and the Magic of Sequences (machinelearningmastery.com) How to use an Encoder-Decoder LSTM to Echo Sequences of Random Integers (google.github.io) tf-seq2seq Python (kdnuggets.com) 7 Steps to Mastering Machine Learning With Python (nbviewer.jupyter.org) An example machine learning notebook Examples (machinelearningmastery.com) How To Implement The Perceptron Algorithm From Scratch In Python (wildml.com) Implementing a Neural Network from Scratch in Python (iamtrask.github.io) A Neural Network in 11 lines of Python (kdnuggets.com) Implementing Your Own k-Nearest Neighbour Algorithm Using Python (machinelearningmastery.com) Demonstration of Memory with a Long Short-Term Memory Network in Python (machinelearningmastery.com) How to Learn to Echo Random Integers with Long Short-Term Memory Recurrent Neural Networks (machinelearningmastery.com) How to Learn to Add Numbers with seq2seq Recurrent Neural Networks Scipy and numpy (scipy-lectures.org) Scipy Lecture Notes (Stanford CS231n) Python Numpy Tutorial (UCSB CHE210D) An introduction to Numpy and Scipy (nbviewer.jupyter.org) A Crash Course in Python for Scientists scikit-learn (nbviewer.jupyter.org) PyCon scikit-learn Tutorial Index (github.com/mmmayo13) scikit-learn Classification Algorithms (scikit-learn.org) scikit-learn Tutorials (github.com/mmmayo13) Abridged scikit-learn Tutorials Tensorflow (tensorflow.org) Tensorflow Tutorials (medium.com/@erikhallstrm) Introduction to TensorFlow — CPU vs GPU (metaflow.fr) TensorFlow: A primer (wildml.com) RNNs in Tensorflow (wildml.com) Implementing a CNN for Text Classification in TensorFlow (surmenok.com) How to Run Text Summarization with TensorFlow PyTorch (pytorch.org) PyTorch Tutorials (gaurav.im) A Gentle Intro to PyTorch (iamtrask.github.io) Tutorial: Deep Learning in PyTorch (github.com/jcjohnson) PyTorch Examples (github.com/MorvanZhou) PyTorch Tutorial (github.com/yunjey) PyTorch Tutorial for Deep Learning Researchers Math (ucsc.edu) Math for Machine Learning (UMIACS CMSC422) Math for Machine Learning Linear algebra (betterexplained.com) An Intuitive Guide to Linear Algebra (betterexplained.com) A Programmer’s Intuition for Matrix Multiplication (betterexplained.com) Understanding the Cross Product (betterexplained.com) Understanding the Dot Product (U. of Buffalo CSE574) Linear Algebra for Machine Learning (medium.com) Linear algebra cheat sheet for deep learning (Stanford CS229) Linear Algebra Review and Reference Probability (betterexplained.com) Understanding Bayes Theorem With Ratios (Stanford CS229) Review of Probability Theory (Stanford CS229) Probability Theory Review for Machine Learning (U. of Buffalo CSE574) Probability Theory (U. of Toronto CSC411) Probability Theory for Machine Learning Calculus (betterexplained.com) How To Understand Derivatives: The Quotient Rule, Exponents, and Logarithms (betterexplained.com) How To Understand Derivatives: The Product, Power & Chain Rules (betterexplained.com) Vector Calculus: Understanding the Gradient (Stanford CS224n) Differential Calculus (readthedocs.io) Calculus Overview Hope you like this article !! Don’t forget to subscribe and like my blogs. Share some love too! Share: Press This Telegram WhatsApp Mohit Sharma(themenyouwanttobe&Co.) Connect with me Telegram My Latest Blog: THEMENYOUWANTTOBE
![What Are Convolution Neural Networks? [ELI5]](https://hackernoon.imgix.net/images/69s32rn.jpg?auto=format&fit=max&w=3840)
