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Microsoft

PyTorch Fundamentals

Microsoft via Microsoft Learn

Overview

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  • Module 1: Learn how to build machine learning models with PyTorch
  • In this module you will:

    • Learn the key concepts used to build machine learning models
    • Learn how to build a Computer Vision model
    • Build models with the PyTorch API
  • Module 2: Learn how to perform different computer vision tasks using PyTorch.
  • In this module you will:

    • Learn how to build computer vision machine learning models
    • Learn how to represent images as tensors
    • Learn how to build Dense Neural Networks and Convolutional Neural Networks
  • Module 3: Learn how to handle language and solve natural language processing tasks with PyTorch
  • In this module you will:

    • Understand how text is processed for natural language processing tasks
    • Get introduced to Recurrent Neural Networks (RNNs) and Generative Neural Networks (GNNs)
    • Learn about Attention Mechanisms
    • Learn how to build text classification models
  • Module 4: Learn how to do audio classification with PyTorch.
  • In this module you will:

    • Learn the basics of audio data
    • Learn how to visualize and transform audio data
    • Build a binary classification speech model that can recognize "yes" and "no"

Syllabus

  • Module 1: Introduction to PyTorch
    • Introduction
    • What are Tensors?
    • Load data with PyTorch Datasets and DataLoaders
    • Transform the data
    • Building the model layers
    • Automatic differentiation
    • Learn about the optimization loop
    • Save, load, and run model predictions
    • The full model building process
    • Summary
  • Module 2: Introduction to Computer Vision with PyTorch
    • Introduction
    • Introduction to processing image data
    • Training a simple dense neural network
    • Training a multi-Layered perceptron
    • Use a convolutional neural network
    • Use a pre-trained network with transfer learning
    • Solving vision problems with MobileNet
    • Summary
  • Module 3: Introduction to Natural Language Processing with PyTorch
    • Introduction
    • Representing text as Tensors
    • Represent words with embeddings
    • Capture patterns with recurrent neural networks
    • Generate text with recurrent networks
    • Attention models and transformers
    • Check your knowledge
    • Summary
  • Module 4: Introduction to Audio Classification with PyTorch
    • Introduction
    • Understand audio data and concepts
    • Audio transforms and visualizations
    • Build the speech model
    • Summary

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