Torch for Machine and Deep Learning培训

课程编码

Torch

Duration

21 hours 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

Overview

火炬是一个开源的机器学习库, 也是一个基于 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。它是机器和深度学习最快、最灵活的框架之一, 被 Facebook、谷歌、推特、NVIDIA、Amd、英特尔等许多公司使用

在这个讲师指导的现场培训中, 我们介绍了火炬的原理、它的独特功能, 以及它如何应用于现实世界的应用。我们在整个过程中完成了大量的动手练习, 展示和实践了所学的概念.

到课程结束时, 学员将全面了解火炬 & #39; 的基本特性和能力, 以及与其他框架和库相比, 火炬在 AI 空间中的作用和贡献。参与者还将获得在自己的项目中实施火炬的必要做法

课程

格式

    机器与深度学习 的
  • 概述
  • 类编码和集成练习
  • 测试问题一直存在, 以检查理解

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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