PyTorch Fundamentals

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

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

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

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"

• 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

• 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

• 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

• Introduction
• Understand audio data and concepts
• Audio transforms and visualizations
• Build the speech model
• Summary