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Torch: Getting started with Machine and Deep Learning培訓
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最新開班 (連續班 、周末班、晚班）：2025年7月14日..用心服務..........--即將開課--...................... |
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課程大綱 |
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- 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
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