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

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

Learn 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.

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Overview

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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 this course, you’ll have a thorough understanding of the concepts and tools necessary to start developing and training your own models using PyTorch.

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

Content

92 Lessons20 Quizzes

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.

Rethinking the Training Loop

17 Lessons

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

Going Classy

15 Lessons

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

A Simple Classification Problem

19 Lessons

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

Conclusion

1 Lessons

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

Appendix

2 Lessons

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

Certificate of Completion

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Course Author:

Daniel Voigt Godoy

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