1、可免费在以后培训班中重听；
        2、免费提供课后技术支持，保障培训效果。
        3、培训合格学员可享受免费推荐就业机会。

• 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