资源集合

deep-learning-resources
深度学习- https://github.com/jiqizhixin/Artificial-Intelligence-Terminology

A Collection of resources I have found useful on my journey finding my way through the world of Deep Learning.
Don't get too hang-up on the perfect road map to Deep Learning Mastery - take something that looks interesting, and get stuck in.
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](https://github.com/chasingbob/deep-learning-resources#courses)Courses
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
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

The fast.ai team (Jeremy Howard & Rachel Thomas) promises to take you to the cool stuff asap.
fast.ai course

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](https://github.com/chasingbob/deep-learning-resources#tutorials--articles)Tutorials & Articles
Preprocessing data
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
Animations done by AlecRadford

An Overview

Recurrent Neural Networks
Understanding LSTM Networks

The Unreasonable Effectiveness of Recurrent Neural Networks ~Andrej Karpathy

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
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](https://github.com/chasingbob/deep-learning-resources#books--e-books)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
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](https://github.com/chasingbob/deep-learning-resources#getting-philosophical)Getting Philosophical
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**
** Replace with Deep Learning expert without changing the point being made by the author
Cardiologist-Level Arrhythmia Detection With Convolutional Neural Networks
When not to use Deep Learning
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](https://github.com/chasingbob/deep-learning-resources#competitions)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:
Digit Recognizer

Dogs vs Cats

The Nature Conservancy Fisheries Monitoring

State Farm Distracted Driver Detection

iNaturalist competition 2017
** The training and validation images weighs in at a hefty 186GB - only for the brave with a monster deep learning machine
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](https://github.com/chasingbob/deep-learning-resources#tools-of-the-trade)Tools of the Trade
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](https://github.com/chasingbob/deep-learning-resources#python)Python
Python Official
Python Programming Tutorials
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](https://github.com/chasingbob/deep-learning-resources#matplotlib)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
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](https://github.com/chasingbob/deep-learning-resources#numpy)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
Intro to Numpy PDF | Jupyter Notebook
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](https://github.com/chasingbob/deep-learning-resources#tensorflow)TensorFlow
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
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](https://github.com/chasingbob/deep-learning-resources#keras)Keras
Keras is a high level framework for Deep Learning that is compatible with both Theano and Tensorflow.
Keras official documentation
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
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](https://github.com/chasingbob/deep-learning-resources#keras-visuals)keras-visuals
Visualise the training of your Keras model with an easy to use Matplotlib graph using one line of code.
keras-visuals
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](https://github.com/chasingbob/deep-learning-resources#datasets)Datasets
20 Weird & Wonderful Datasets for Machine Learning
Enron Email Dataset
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