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7 Steps to Mastering Machine Learning With Python (

An example machine learning notebook (


How To Implement The Perceptron Algorithm From Scratch In Python (

Implementing a Neural Network from Scratch in Python (

A Neural Network in 11 lines of Python (

Implementing Your Own k-Nearest Neighbour Algorithm Using Python (

Demonstration of Memory with a Long Short-Term Memory Network in Python (

How to Learn to Echo Random Integers with Long Short-Term Memory Recurrent Neural Networks (

How to Learn to Add Numbers with seq2seq Recurrent Neural Networks (

Scipy and numpy

Scipy Lecture Notes (

Python Numpy Tutorial (Stanford CS231n)

An introduction to Numpy and Scipy (UCSB CHE210D)

A Crash Course in Python for Scientists (


PyCon scikit-learn Tutorial Index (

scikit-learn Classification Algorithms (

scikit-learn Tutorials (

Abridged scikit-learn Tutorials (


Tensorflow Tutorials (

Introduction to TensorFlow — CPU vs GPU (

TensorFlow: A primer (

RNNs in Tensorflow (

Implementing a CNN for Text Classification in TensorFlow (

How to Run Text Summarization with TensorFlow (


PyTorch Tutorials (

A Gentle Intro to PyTorch (

Tutorial: Deep Learning in PyTorch (

PyTorch Examples (

PyTorch Tutorial (

PyTorch Tutorial for Deep Learning Researchers (

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Yes you should understand backprop (

Can you give a visual explanation for the back propagation algorithm for neural networks? (

How the backpropagation algorithm works (

Backpropagation Through Time and Vanishing Gradients (

A Gentle Introduction to Backpropagation Through Time (

Backpropagation, Intuitions (Stanford CS231n)

Deep Learning

Deep Learning in a Nutshell (

A Tutorial on Deep Learning (Quoc V. Le)

What is Deep Learning? (

What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning? (

Optimization and Dimensionality Reduction

Seven Techniques for Data Dimensionality Reduction (

Principal components analysis (Stanford CS229)

Dropout: A simple way to improve neural networks (Hinton @ NIPS 2012)

How to train your Deep Neural Network (

Long Short Term Memory (LSTM)

A Gentle Introduction to Long Short-Term Memory Networks by the Experts (

Understanding LSTM Networks (

Exploring LSTMs (

Anyone Can Learn To Code an LSTM-RNN in Python (

Convolutional Neural Networks (CNNs)

Introducing convolutional networks (

Deep Learning and Convolutional Neural Networks (

Conv Nets: A Modular Perspective (

Understanding Convolutions (

Recurrent Neural Nets (RNNs)

Recurrent Neural Networks Tutorial (

Attention and Augmented Recurrent Neural Networks (

The Unreasonable Effectiveness of Recurrent Neural Networks (

A Deep Dive into Recurrent Neural Nets (

Reinforcement Learning

Simple Beginner’s guide to Reinforcement Learning & its implementation (

A Tutorial for Reinforcement Learning (

Learning Reinforcement Learning (

Deep Reinforcement Learning: Pong from Pixels (

Generative Adversarial Networks (GANs)

What’s a Generative Adversarial Network? (

Abusing Generative Adversarial Networks to Make 8-bit Pixel Art (

An introduction to Generative Adversarial Networks (with code in TensorFlow) (

Generative Adversarial Networks for Beginners (

Multi-task Learning

An Overview of Multi-Task Learning in Deep Neural Networks (

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Machine Learning

Machine Learning is Fun! (

Machine Learning Crash Course: Part IPart IIPart III (Machine Learning at Berkeley)

An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples (

A Gentle Guide to Machine Learning (

Which machine learning algorithm should I use? (

Activation and Loss Functions

Sigmoid neurons (

What is the role of the activation function in a neural network? (

Comprehensive list of activation functions in neural networks with pros/cons (

Activation functions and it’s types-Which is better? (

Making Sense of Logarithmic Loss (

Loss Functions (Stanford CS231n)

L1 vs. L2 Loss function (

The cross-entropy cost function (


Role of Bias in Neural Networks (

Bias Nodes in Neural Networks (

What is bias in artificial neural network? (


Perceptrons (

The Perception (

Single-layer Neural Networks (Perceptrons) (

From Perceptrons to Deep Networks (


Introduction to linear regression analysis (

Linear Regression (

Linear Regression (

Logistic Regression (

Simple Linear Regression Tutorial for Machine Learning (

Logistic Regression Tutorial for Machine Learning (

Softmax Regression (

Gradient Descent

Learning with gradient descent (

Gradient Descent (

How to understand Gradient Descent algorithm (

An overview of gradient descent optimization algorithms (

Optimization: Stochastic Gradient Descent (Stanford CS231n)

Generative Learning

Generative Learning Algorithms (Stanford CS229)

A practical explanation of a Naive Bayes classifier (

Support Vector Machines

An introduction to Support Vector Machines (SVM) (

Support Vector Machines (Stanford CS229)

Linear classification: Support Vector Machine, Softmax (Stanford 231n)