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
Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. by Geoffrey Hinton
Deep Learning is an important subset of Machine Learning and it is therefor important to get a wider knowledge and understanding of Machine Learning. The Coursera Machine Learning course by Andrew Ng is highly recommended.
The fast.ai team (Jeremy Howard & Rachel Thomas) promises to take you to the cool stuff asap.
You know when Chris Olah is involved it will be brilliant - one of the best posts on visualising how Neural Networks learn to see around.
Image Kernels - Explained visually
How to setup your Windows 10 machine for Machine Learning using Ubuntu Bash shell
A 'Brief' History of Neural Nets and Deep Learning (4 parts)
YouTube: Excellent visualization of How Neural Networks Work
Tinker with a Neural Network Right Here in Your Browser - Tensorflow Playground
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
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The Unreasonable Effectiveness of Recurrent Neural Networks ~Andrej Karpathy
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YouTube: Recurrent Neural Networks (RNN) and and Long Short-Term Memory (LSTM)
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
Jupyter notebook - DeepDreaming with TensorFlow
The Black Magic of Deep Learning - Tips and Tricks for the practitioner
YouTube: Dimensionality Reduction: Principal Component Analysis, Part 1 | Part 2 | Part 3
Hands-On Machine Learning with Scikit-Learn and Tensorflow
Neural Networks and Deep Learning
Deep Learning Book - some call this book the Deep Learning bible
Diverse AI applications around the world
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?
Visualising a Neural Network as a tree with branches and using smart pruning techniques might be the answer to getting a peek view of what is going on inside the 'black box' of a Neural Network
A One-Step Program for Becoming a Data Scientist**
** Is interchangeable with Deep Learning Expert
Cardiologist-Level Arrhythmia Detection With Convolutional Neural Networks
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:
** The training and validation images weighs in at a hefty 186GB - only for the brave with a monster deep learning machine
YouTube: How did Python become a data science powerhouse?
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.
Intro to Numpy PDF | Jupyter Notebook
Pandas is a high level data manipulation tool based on the Numpy package. At it's core Pandas uses a DataFrame which allows you to store and manipulate tabular data.
TensorFlow is an open source software library for numerical computation using data flow graphs. TensorFlow is designed and highly optimised to take advantage of GPU technology in a distributed manner not only on a single instance with many GPU's, but also across many devices and networks, making it an ideal framework for learning and production.
TensorFlow official documentation
Getting Started With TensorFlow
Learn TensorFlow and deep learning, without a Ph.D.
Installing TensorFlow on a Raspberry Pi 3
Keras is a high level framework for Deep Learning that is 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
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
11k Hands - Gender recognition and biometric identification using a large dataset of hand images
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
Adit Deshpande | Blog | Twitter
Josh Gordon | Twitter
Brandon Rohrer | Blog | Twitter
Rachel Thomas | Blog | Twitter