Deep Learning with PyTorch Step-by-Step: Part I - Fundamentals

This course is ideal for anyone who wants to learn PyTorch, starting from PyTorch basics and expanding to use PyTorch for deep learning.

Beginner

92 Lessons

Certificate of Completion

This course is ideal for anyone who wants to learn PyTorch, starting from PyTorch basics and expanding to use PyTorch for deep learning.

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Explanations

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Explanations

This course includes

184 Playgrounds

20 Quizzes

This course includes

184 Playgrounds

20 Quizzes

Course Overview

This course is designed to provide you with an easy-to-follow, structured, incremental, and from-first-principles approach to learning PyTorch. In this course, you’ll be introduced to the fundamentals of PyTorch: autograd, model classes, datasets, data loaders, and more. You will develop, step-by-step, not only the models themselves but also your understanding of them. You'll be shown both the reasoning behind the code and how to avoid some common pitfalls and errors along the way. By the time you finish th...Show More

TAKEAWAY SKILLS

Python

Machine Learning

Deep Learning

Neural Networks

Pytorch

Course Content

Introduction

In this chapter, you will learn why PyTorch is ideal for Deep Learning and get an overview of the course, including benefits, prerequisites, and key details.

Why PyTorch?

Course Overview

Visualizing Gradient Descent

This chapter explores how to use gradient descent, covering model initialization, loss computation, parameter updates, and the importance of learning rates.

Spoilers

Gradient Descent

Data Generation Basic Steps for Using Gradient Descent (Step 0 and 1)Step 2a - Compute the Loss Step 2b - Computing the Loss Surface Step 3 - Compute the Gradients Step 4 - Update the Parameters Learning Rate Scaling the Dataset Step 5 - Rinse and Repeat!Recap Quiz Challenge 1 - Visualizing Gradient Descent Solution Review - Visualizing Gradient Descent

A Simple Regression Problem

In this chapter, you will learn to implement linear regression in PyTorch, focusing on gradient descent, tensor operations, model building, and optimization.

Rethinking the Training Loop

In this chapter, you will discover how to enhance PyTorch training loops with mini-batch processing, evaluation techniques, and effective model management.

Spoilers Introducing Higher-Order Functions Rethinking the Training Loop Building a Dataset DataLoader Quiz Mini-Batch Inner Loop and Training Split Evaluation TensorBoard SummaryWriter Saving and Loading Models Deploying / Making Predictions Putting It All Together Recap Quiz Challenge 3 - Rethinking the Training Loop Solution Review - Rethinking the Training Loop

Going Classy

In this chapter, you’ll build a PyTorch class for training, handle methods/functions, save/load models, use checkpoints, and integrate the Classy pipeline.

Spoilers Going Classy Functions

Different Method Types Training Methods Saving and Loading Methods Finalising the Methods needed for going Classy Classy Pipeline Model Training and Predictions Checkpointing Putting It All Together Recap Quiz Challenge 4 - Going Classy Solution Review - Going Classy

A Simple Classification Problem

19 Lessons

In this chapter, you will explore binary classification with PyTorch, including model configuration, loss functions, metrics, and decision boundaries.

Conclusion

1 Lesson

In this chapter, you celebrate finishing the Deep Learning course with PyTorch, reflect on your skills, and get encouraged to continue learning.

Appendix

2 Lessons

This chapter covers how to set up Jupyter notebooks using Google Colab, Binder, or Anaconda, manage environments, and run TensorBoard with GPU support.

Course Author

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