课程大纲
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
要求
- 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
客户评论 (3)
在“现实生活”的例子中展示了大量的实践知识。
Kamil - Streamsoft Kraków
课程 - Java Advanced
机器翻译
定期推送更改,因为在第 3 天我开始比以前更迷茫,并且更难快速发现错误,我能够快速查看最新的更改并及时了解材料
Paulina
课程 - Advanced Java Security
机器翻译
Very good knowledge and character.