Course Outline

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

Requirements

  • Programming experience in any language.
  • A general familiarity with C/C++ helps.
  • An interest in Artificial Intelligence (AI).

Audience

  • Software developers and programmers wishing to enable Machine and Deep Learning within their applications
 21 Hours

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