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Torch: Getting started with Machine and Deep Learning培训

 
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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
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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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