Foundations of PyTorch

سرفصل های دوره

This course covers many aspects of building deep learning models in PyTorch, including neurons and neural networks, and how PyTorch uses differential calculus to train such models and create dynamic computation graphs in deep learning.

1. Course Overview

1. Course Overview

2. Getting Started with PyTorch for Machine Learning

01. Version Check

02. Module Overview

03. Prerequisites and Course Outline

04. Representation Learning Using Neural Networks

05. Neuron as a Mathematical Function

06. Activation Functions

07. Introducing PyTorch

08. TensorFlow and PyTorch

09. Demo - PyTorch Install and Setup

10. Summary

3. Working PyTorch Tensors

01. Module Overview

02. Demo - Creating and Initializing Tensors

03. Demo - Simple Operations on Tensors

04. Demo - Elementwise and Matrix Operations on Tensors

05. Demo - Converting between PyTorch Tensors and NumPy Arrays

06. PyTorch Support for CUDA Devices

07. Demo - Setting up a Deep Learning VM to Work with GPUs

08. Demo - Creating Tensors on CUDA-enabled Devices

09. Demo - Working with the Device Context Manager

10. Summary

4. Working with Gradients Using the Autograd Library

01. Module Overview

02. Gradient Descent Optimization

03. Forward and Backward Passes

04. Calculating Gradients

05. Using Gradients to Update Model Parameters

06. Two Passes in Reverse Mode Automatic Differentiation

07. Demo - Introducing Autograd

08. Demo - Working with Gradients

09. Demo - Variables and Tensors

10. Demo - Training a Linear Model Using Autograd

11. Summary

5. Building Dynamic Computation Graphs

01. Module Overview

02. Static vs. Dynamic Computation Graphs

03. Dynamic Computation Graphs in PyTorch

04. Demo - Installing Tensorflow, Graphviz, and Hidden Layer

05. Demo - Building Dynamic Computations Graphs with PyTorch

06. Demo - Visualizing Neural Networks in PyTorch Using Hidden Layer

07. Demo - Building Static Computation Graphs with Tensorflow

08. Demo - Visualizing Tensorflow Graphs with Tensorboard

09. Demo - Dynamic Computation Graphs in Tensorflow with Eager Execution

10. Debugging in PyTorch and Tensorflow

11. Summary and Further Study

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تولید کننده: PluralSight