Torch for Machine and Deep Learning Treningskurs

Kurskode

Torch

Varighet

21 timer (vanligvis 3 dag inkludert pauser)

Krav

  • 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

Oversikt

Torch er et bibliotek med åpen kildekode og et vitenskapelig datarammeverk basert på programmeringsspråket Lua . Det gir et utviklingsmiljø for numerikk, maskinlæring og datasyn, med særlig vekt på dyp læring og sammenvevingsnett. Det er et av de raskeste og mest fleksible rammene for Machine and Deep Learning og brukes av selskaper som Facebook , Go ogle, Twitter, NVIDIA, AMD, Intel og mange andre.

I denne instruktørledede liveopplæringen dekker vi prinsippene for Torch , dens unike funksjoner og hvordan den kan brukes i virkelige applikasjoner. Vi går gjennom en rekke praktiske øvelser gjennom hele tiden, demonstrerer og praktiserer de innlærte konseptene.

Mot slutten av kurset vil deltakerne ha en grundig forståelse av Torch underliggende funksjoner og evner, så vel som dens rolle og bidrag innenfor AI-rommet sammenlignet med andre rammer og biblioteker. Deltakerne vil også ha fått nødvendig praksis for å implementere Torch i sine egne prosjekter.

    Kursets format

    • Oversikt over maskin og Deep Learning
    • Kodings- og integrasjonsøvelser i klassen
    • Testspørsmål strødd underveis for å sjekke forståelse

    Machine Translated

    Kursplan

    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

    Testimonials

    ★★★★★
    ★★★★★

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