Torch: Getting started with Machine and Deep Learning培训




21 hours 通常来说是3天,包括中间休息。


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


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


In this instructor-led, live training, we cover the principles of Torch, its unique features, and how it can be applied in real-world applications. We step through numerous hands-on exercises all throughout, demonstrating and practicing the concepts learned.

By the end of the course, participants will have a thorough understanding of Torch's underlying features and capabilities as well as its role and contribution within the AI space compared to other frameworks and libraries. Participants will have also received the necessary practice to implement Torch in their own projects.

Format of the course

  • Overview of Machine and Deep Learning
  • In-class coding and integration exercises
  • Test questions sprinkled along the way to check understanding


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

  • 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







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