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Torch: Getting started with Machine and Deep Learning培訓(xùn)
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開課地址：【上海】同濟(jì)大學(xué)(滬西)/新城金郡商務(wù)樓(11號(hào)線白銀路站)【深圳分部】：電影大廈(地鐵一號(hào)線大劇院站) 【武漢分部】：佳源大廈【成都分部】：領(lǐng)館區(qū)1號(hào)【沈陽(yáng)分部】：沈陽(yáng)理工大學(xué)【鄭州分部】：錦華大廈【石家莊分部】：瑞景大廈【北京分部】：北京中山學(xué)院 【南京分部】：金港大廈
最新開班 (連續(xù)班 、周末班、晚班）：2020年3月16日 |
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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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