Corso di formazione Torch for Machine and Deep Learning

  1. Artificial Intelligence Training
  2. Machine Learning Training
  3. Deep Learning Training
  4. Torch Training
  5. Corso di formazione Torch for Machine and Deep Learning

Codice del corso

Torch

Durata

21 ore (generalmente 3 giorni pause incluse)

Requisiti

  • 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

Torch è una libreria di apprendimento automatico open source e un framework di calcolo scientifico basato sul Lua programmazione Lua . Fornisce un ambiente di sviluppo per la numerica, l'apprendimento automatico e la visione artificiale, con particolare enfasi sull'apprendimento profondo e sulle reti convoluzionali. È uno dei framework più veloci e flessibili per Machine e Deep Learning ed è utilizzato da aziende come Facebook , Go ogle, Twitter, NVIDIA, AMD, Intel e molti altri.

In questo training dal vivo con istruttore, copriamo i principi di Torch , le sue caratteristiche uniche e come può essere applicato in applicazioni del mondo reale. Eseguiamo numerosi esercizi pratici per tutto il tempo, dimostrando e praticando i concetti appresi.

Entro la fine del corso, i partecipanti avranno una conoscenza approfondita delle caratteristiche e delle capacità sottostanti di Torch , nonché del suo ruolo e contributo all'interno dello spazio AI rispetto ad altri framework e librerie. I partecipanti avranno inoltre ricevuto le pratiche necessarie per implementare Torch nei propri progetti.

    Formato del corso

    • Panoramica di Machine e Deep Learning
    • Esercitazioni di codifica e integrazione in classe
    • Testare le domande sparse lungo il percorso per verificare la comprensione

    Machine Translated

    Struttura del corso

    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

    Recensioni

    ★★★★★
    ★★★★★

    Categorie relative

    Corsi relativi

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