Plan du cours

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

Pré requis

  • 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 heures
 

Nombre de participants


Début

Fin


Dates are subject to availability and take place between 09:30 and 16:30.
Les formations ouvertes requièrent plus de 3 participants.

Nos Clients témoignent (4)

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