Torch for Machine and Deep Learning Training Course

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