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PyTorch Fundamentals

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Free Online Course: PyTorch Fundamentals provided by Microsoft Learn is a comprehensive online course, which lasts for 4-5 hours worth of material. The course is taught in English and is free of charge.

Microsoft via Microsoft Learn
4-5 hours worth of material
Self Paced
Overview

    • 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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