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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.
4.6
92 Lessons
8h
Join 2.9 million developers at
Join 2.9 million developers at
Learning Roadmap
1.
Introduction
Introduction
Get familiar with PyTorch's pythonic nature and foundational concepts designed for beginners.
2.
Visualizing Gradient Descent
Visualizing Gradient Descent
Unpack the core of visualizing gradient descent, exploring parameter updates, learning rates, and feature scaling using a linear regression model in Numpy.
SpoilersGradient DescentData GenerationBasic Steps for Using Gradient Descent (Step 0 and 1)Step 2a - Compute the LossStep 2b - Computing the Loss SurfaceStep 3 - Compute the GradientsStep 4 - Update the ParametersLearning RateScaling the DatasetStep 5 - Rinse and Repeat!RecapQuizChallenge 1 - Visualizing Gradient DescentSolution Review - Visualizing Gradient Descent
3.
A Simple Regression Problem
A Simple Regression Problem
21 Lessons
21 Lessons
Master the steps to implement linear regression with PyTorch, covering tensors, autograd, optimizers, and model creation.
4.
Rethinking the Training Loop
Rethinking the Training Loop
17 Lessons
17 Lessons
Grasp the fundamentals of creating an effective training loop in PyTorch.
5.
Going Classy
Going Classy
15 Lessons
15 Lessons
Deepen your knowledge of creating and integrating a PyTorch class, enhancing code management.
6.
A Simple Classification Problem
A Simple Classification Problem
19 Lessons
19 Lessons
See how it works to build and evaluate a binary classification model using PyTorch.
8.
Appendix
Appendix
2 Lessons
2 Lessons
Get familiar with setting up Jupyter notebooks and running TensorBoard for deep learning tasks.
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ABOUT THIS COURSE
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.
ABOUT THE AUTHOR
Daniel Voigt Godoy
Daniel is a data scientist, teacher, and author of "Deep Learning with PyTorch Step-by-Step: A Beginner's Guide". He has been teaching machine learning and distributed computing technologies since 2016.
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