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.

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] (https://www.cs.cmu.edu/~enron/)

##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