Home>Courses>Deep Learning with PyTorch Step-by-Step: Part I - Fundamentals

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

Delve into PyTorch basics: autograd, model classes, datasets, and data loaders. Gain insights into model development while avoiding common pitfalls. Start creating and training your own PyTorch models.

Beginner

92 Lessons

Certificate of Completion

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

184 Playgrounds

20 Quizzes

Course Overview

Course Content

Apply Your Skills

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

This course is designed to provide you with an easy-to-follow, structured, incremental, and from-first-principles approach to le...Show More

TAKEAWAY SKILLS

Python

Machine Learning

Deep Learning

Neural Networks

PyTorch

Course Content

Introduction

2 Lessons

Get familiar with PyTorch's pythonic nature and foundational concepts designed for beginners.

Why PyTorch?

Course Overview

Visualizing Gradient Descent

15 Lessons

Unpack the core of visualizing gradient descent, exploring parameter updates, learning rates, and feature scaling using a linear regression model in Numpy.

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

21 Lessons

Master the steps to implement linear regression with PyTorch, covering tensors, autograd, optimizers, and model creation.

Spoilers

Reviewing the Steps of Gradient Descent

Linear Regression in Numpy

PyTorch Tensors

Loading Data, Devices, and CUDA

Dynamic Computation Graphs

Putting It All Together

Recap

Quiz

Challenge 2 - A Simple Regression Problem

Solution Review: A Simple Regression Problem

Rethinking the Training Loop

17 Lessons

Grasp the fundamentals of creating an effective training loop in PyTorch.

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

15 Lessons

Deepen your knowledge of creating and integrating a PyTorch class, enhancing code management.

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

See how it works to build and evaluate a binary classification model using PyTorch.

Spoilers

Classification Problems

Model for Classification Problems

Sigmoid and Logistic Regression

Quiz

Loss

Binary Cross-Entropy Loss in PyTorch

Imbalanced Dataset

Model Configuration, Training, and Predictions for Classification

Decision Boundary

Classification Threshold and Confusion Matrix

Metrics

Trade-offs and Curves

Best + Worst Curves and Models

Putting it All Together

Recap

Quiz

Challenge 5 - A Simple Classification Problem

Solution Review: A Simple Classification Problem

Conclusion

1 Lessons

Build on your deep learning skills by exploring further and staying engaged.

What's Next?

Appendix

2 Lessons

Get familiar with setting up Jupyter notebooks and running TensorBoard for deep learning tasks.

Course Author

Text Summarization with Transformers

intermediate

1 hour

Project

Text Summarization with Transformers

Learn to summarize text using the BART transformer model, focusing on pre-training techniques and evaluation methods.

intermediate

1 hour

Project

Using a PyTorch Model in JavaScript with ONNX

intermediate

1 hour

Project

Using a PyTorch Model in JavaScript with ONNX

Learn to convert a PyTorch ResNet-18 model to ONNX for use in JavaScript for image classification tasks.

intermediate

1 hour

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