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Neural networks for hackers
Introduction
What is 'deep learning'?
Multi-layer perceptron
Inputs
Convnets
Text and sequences
Outputs and tasks
Classification
Translation
Generation
Retrieval
Reinforcement learning
GPUs and Frameworks
Getting started
Mathematical Topics
Neurons
Backpropagation
Error
Gradients
Learning methods
Loss functions
Convolutional neural networks
Designing convnets
AlexNet
Maxpool
GoogLeNet
Spatial transformer
Style Transfer
Generative Adversarial Networks
Applications
Highway Networks (screencast)
Variational Autoencoders
Introduction
Training
Context Encoders
Denoising Autoencoder
Context Encoder Part 1 (screencast)
Context Encoder Part 2 (screencast)
Natural Language Processing
Word Embeddings
Skip-thought Vectors Part 1 (Screencast)
Skip-thought Vectors Part 2 (Screencast)
Recurrent neural network
Memory units
Translation
City Name Generation (screencast)
Reinforcement Learning
Introduction
Deep Q-learning
Policies
Improvements on DQN
Playing Ms. Pacman Part 1 (screencast)
Playing Ms Pacman Part 2 (Screencast)
Simulators
Solving League of Legends (screencast)
Hacks and Optimizations
Introduction
Hyperparameter optimization
Training Algorithms
Dropout
Synthetic Gradients
Normalization
Activation Maximization
Saliency Maps
t-SNE
Conclusion
3D pose from video (Screencast)
Further Reading
Teach online with
Denoising Autoencoder
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