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Torch: Getting started with Machine and Deep Learning培训
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 班级规模及环境--热线:4008699035 手机:15921673576( 微信同号) |
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坚持小班授课，为保证培训效果，增加互动环节，每期人数限3到5人。 |
| 上课时间和地点 |
开课地址：【上海】同济大学(沪西)/新城金郡商务楼(11号线白银路站)【深圳分部】：电影大厦(地铁一号线大剧院站) 【武汉分部】：佳源大厦【成都分部】：领馆区1号【沈阳分部】：沈阳理工大学【郑州分部】：锦华大厦【石家庄分部】：瑞景大厦【北京分部】：北京中山学院 【南京分部】：金港大厦
最新开班 (连续班 、周末班、晚班）：请点击此处咨询在线客服 |
| 实验设备 |
☆资深工程师授课
☆注重质量
☆边讲边练
☆合格学员免费推荐工作
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| 质量保障 |
1、培训过程中，如有部分内容理解不透或消化不好，可免费在以后培训班中重听；
2、课程完成后,授课老师留给学员手机和Email,保障培训效果,免费提供课后答疑。
3、培训合格学员可享受免费推荐就业机会。 |
课程大纲 |
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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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