1、培訓過程中，如有部分內容理解不透或消化不好，可免費在以后培訓班中重聽；
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Introduction to Torch

Like NumPy but with CPU and GPU implementation
Torch's usage in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking
Installing Torch

Linux, Windows, Mac
Bitmapi and Docker
Installing Torch packages

Using the LuaRocks package manager
Choosing an IDE for Torch

ZeroBrane Studio
Eclipse plugin for Lua
Working with the Lua scripting language and LuaJIT

Lua's integration with C/C++
Lua syntax: datatypes, loops and conditionals, functions, functions, tables, and file i/o.
Object orientation and serialization in Torch
Coding exercise
Loading a dataset in Torch

MNIST
CIFAR-10, CIFAR-100
Imagenet
Machine Learning in Torch

Deep Learning
Manual feature extraction vs convolutional networks
Supervised and Unsupervised Learning
Building a neural network with Torch
N-dimensional arrays
Image analysis with Torch

Image package
The Tensor library
Working with the REPL interpreter

Working with databases

Networking and Torch

GPU support in Torch

Integrating Torch

C, Python, and others
Embedding Torch

iOS and Android
Other frameworks and libraries

Facebook's optimized deep-learning modules and containers
Creating your own package

Testing and debugging

Releasing your application

The future of AI and Torch

Summary and Conclusion