Learn and practice PyTorch, a framework for developing deep learning models.

Become a Deep Learning Professional

In this module, we’ll be introduced to the fundamentals of PyTorch: autograd, model classes, datasets, data loaders, and more. We’ll develop models and understand them step-by-step. We'll understand the reasoning behind the code and how to avoid some common pitfalls and errors along the way.
By the time we finish this module, we’ll have a thorough understanding of the concepts and tools necessary to start developing and training our own models using PyTorch.

Deep Learning with PyTorch

## Introduction to gradient descent

According to [Wikipedia](https://en.wikipedia.org/wiki/Gradient_descent): 

> "Gradient descent is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function."

But if I were to describe it, I would say:

> Gradient descent is an iterative technique commonly used in Machine Learning and Deep Learning to try to find the best possible set of parameters/coefficients for a given model, data points, and loss function, starting from an initial, and usually, random guess.

# Introduction to gradient descent

According to [Wikipedia](https://en.wikipedia.org/wiki/Gradient_descent): 

Learn about what gradient descent is, why visualizing it is important, and the model being used. 

Gradient Descent

Learn about what gradient descent is, why visualizing it is important, and the model being used.

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Appendix

Conclusion

Gradient Descent

Introduction to gradient descent