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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