A Collection of resources I have found useful on my journey finding my way through the world of Deep Learning.
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.
YouTube: Excellent visualization of How Neural Networks Work
Tinker with a Neural Network Right Here in Your Browser
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 framework for Deep Learning that compatible with both Theano and Tensorflow.
-
The Keras Blog - Building powerful image classification models using very little data
-
How convolutional neural networks see the world ~Francois Chollet
-
A complete guide to using Keras as part of a TensorFlow workflow
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
PDF: Lip reading using CNN and LSTM
Running Jupyter notebooks on GPU on AWS: a starter guide
Neural Networks and Deep Learning
Deep Learning Book - some call this book the Deep Learning bible
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?
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:
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
YouTube: Bare Minimum: Matplotlib for data visualization
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.
Visualise the training of your Keras model with an easy to use Matplotlib graph using one line of code.
20 Weird & Wonderful Datasets for Machine Learning
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