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Collection of resources => Deep Learning

https://github.com/chasingbob/deep-learning-resources

deep-learning-resources

A Collection of resources I have found useful on my journey finding my way through the world of Deep Learning.

Courses

Stanford CS231n Convolutional Neural Networks for Visual Recognition

Coursera: Neural Networks for Machine Learning

Even though Deep Learning is a small but important subset of Machine Learning, it is still important to get a wider knowledge and understanding of Machine Learning and no course will give you a better understanding than the excellent course by Andrew Ng.

Coursera: Machine Learning

Tutorials

A Beginner's Guide To Understanding Convolutional Neural Networks

An Intuitive Explanation of Convolutional Neural Networks

Hacker's guide to Neural Networks ~Andrej Karpathy

Gradient Descent Optimisation Algorithms

Recurrent Neural Networks

Keras is my favorite framework for Deep Learning and is underneath compatible with both Theano and Tensorflow.

TensorFlow

A Few Useful Things to Know about Machine Learning ~Pedro Domingos

YouTube: Introduction to Deep Learning with Python

YouTube: Machine Learning with Python

YouTube: Deep Visualization Toolbox

Yes you should understand backprop ~Andrej Karpathy

PDF: Dropout: A Simple Way to Prevent Neural Networks from Overfitting

PDF: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and 0.5MB model size

Quora: How does a confusion matrix work

PDF: Understanding the difficulty of training deep feedforward neural networks

Books & e-Books

Neural Networks and Deep Learning

Deep Learning Book - some call this book the Deep Learning bible

Machine Learning Yearning - Technical Strategy for AI Engineers, in the Era of Deep Learning ~Andrew Ng

Getting Philosophical

What is the next likely breakthrough in Deep Learning

Looking at The major advancements in Deep Learning in 2016 gives us a peek into the future of deep learing. A big portion of the effort went into Generative Models, let us see if that is the case in 2017.

Do machines actually beat doctors?

Competitions

Kaggle is the place to be for Data Scientists and Deep Learning experts at the moment - but you don't have to be an expert to feel the adrenalin of a $150000 competition

Kaggle competitions perfect for deep learning:

Tools of the Trade

Python

Python Official

Python Programming Tutorials

MatplotLib

Deep Learning is far from being an exact science and a lot of what you do is based on getting a feel for the underlying mechanics, visualising the moving parts makes it easier to understand and Matplotlib is the go-to library for visualisation

Matplotlib official

Matplotlib tutorial

YouTube: Bare Minimum: Matplotlib for data visualization

NumPy

NumPy is a fast optimized package for scientific computing, and is also the underlying library a lot of Machine Learning frameworks are build on top of. Becoming a NumPy ninja is an important step to mastery.

NumPy official

CS231n Python Numpy Tutorial

100 NumPy exercises

keras-visuals

Visualise the training of your Keras model with an easy to use Matplotlib graph using one line of code.

keras-visuals

Datasets

20 Weird & Wonderful Datasets for Machine Learning

Enron Email Dataset

Whom I follow

Andrew Ng | Homepage | Twitter

François Chollet | Homepage | Github Twitter

Ian Goodfellow | Homepage | Github | Twitter

Tshilidzi Mudau | Twitter

Yann LeCun | Yann LeCun | Twitter | Quora

Mike Tyka | Homepage | Twitter

Jason Yosinski | Homepage | Twitter | Youtube

Andrej Karpathy | Homepage | Twitter | G+

Chris Olah | Homepage | Github | Twitter

Yoshua Bengio | Homepage

Hugo Larochelle | Homepage | Twitter

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