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Torch: Getting started with Machine and Deep Learning培训

 
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   上课时间和地点
上课地点:【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)/深圳大学成教院 【北京分部】:北京中山/福鑫大楼 【南京分部】:金港大厦(和燕路) 【武汉分部】:佳源大厦(高新二路) 【成都分部】:领馆区1号(中和大道) 【沈阳分部】:沈阳理工大学/六宅臻品 【郑州分部】:郑州大学/锦华大厦 【石家庄分部】:河北科技大学/瑞景大厦 【广州分部】:广粮大厦 【西安分部】:协同大厦
近开课时间(周末班/连续班/晚班):2019年1月26日
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        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;
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        3、培训合格学员可享受免费推荐就业机会。

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

 
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