https://github.com/chasingbob/deep-learning-resources/blob/master/README.md
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
Gradient Descent Optimisation Algorithms
Animations done by AlecRadford
An Overview
Understanding LSTM Networks
Keras is my favorite framework for Deep Learning and is underneath compatible with both Theano and Tensorflow.
The Keras Blog - Building powerful image classification models using very little data
A Few Useful Things to Know about Machine Learning ~Pedro Domingos
How convolutional neural networks see the world ~Francois Chollet
Youtube
Introduction to Deep Learning with Python
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 $120000 competition
Kaggle competitions perfect for deep learning:
Digit Recognizer
Dogs vs Cats
The Nature Conservancy Fisheries Monitoring
State Farm Distracted Driver Detection
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
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
100 NumPy exercises
Datasets
20 Weird & Wonderful Datasets for Machine Learning
Enron Email Dataset
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
Gradient Descent Optimisation Algorithms
Animations done by AlecRadford
An Overview
Understanding LSTM Networks
Keras is my favorite framework for Deep Learning and is underneath compatible with both Theano and Tensorflow.
The Keras Blog - Building powerful image classification models using very little data
A Few Useful Things to Know about Machine Learning ~Pedro Domingos
How convolutional neural networks see the world ~Francois Chollet
Youtube
Introduction to Deep Learning with Python
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 $120000 competition
Kaggle competitions perfect for deep learning:
Digit Recognizer
Dogs vs Cats
The Nature Conservancy Fisheries Monitoring
State Farm Distracted Driver Detection
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
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
100 NumPy exercises
Datasets
20 Weird & Wonderful Datasets for Machine Learning
Enron Email Dataset
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