Torch for Machine and Deep Learning培训

课程编码

Torch

课程时长

21 小时 通常来说是3天,包括中间休息。

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

课程概览

Torch是一个开源的机器学习库和基于Lua编程语言的科学计算框架。它为数字,机器学习和计算机视觉提供了一个开发环境,特别强调深度学习和卷积网络。它是机器和Deep Learning速度最快,最灵活的框架之一,被FacebookGo ogle,Twitter,NVIDIA,AMD,英特尔等公司使用。

在这个以讲师为主导的现场培训中,我们将介绍Torch的原理,其独特的功能以及如何将其应用于实际应用中。我们在整个过程中逐步进行多次动手练习,展示和练习所学的概念。

在课程结束时,与其他框架和库相比,参与者将全面了解Torch的基本特性和功能,以及它在AI空间中的作用和贡献。参与者还将获得在他们自己的项目中实施Torch的必要实践。

    课程格式

    • 机器和Deep Learning概述
    • 课堂编码和集成练习
    • 沿途撒有测试题以检查理解

    Machine Translated

    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

    客户评论

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