Deep Machine Learning Using PyTorch

Wishlist Share
Share Course
Page Link
Share On Social Media

About Course

PyTorch is an open source machine learning library for Python that facilitates building deep learning projects. We’ve published a 10-hour course that will take you from being complete beginner in PyTorch to using it to code your own GANs (generative adversarial networks). You don’t even have to know what a GAN is to start!

This coding-first course is approachable to people starting out with deep learning and neural networks. The course was developed by Aakash. This is a comprehensive course and it covers the following topics:

  • PyTorch Basics & Linear Regression
  • Image Classification with Logistic Regression
  • Training Deep Neural Networks on a GPU with PyTorch
  • Image Classification using Convolutional Neural Networks
  • Residual Networks, Data Augmentation and Regularization
  • Training Generative Adversarial Networks (GANs)

There is code and detailed notes to go along with each section of this course. You can access the code in Jupyter Notebooks that are provided. This allows you to try the code yourself at each step of the way.

If you have been wanting to learn more about deep learning but haven’t known where to start, this is a great place to begin your journey of learning about deep learning. It will be helpful to have a basic understanding of Python before you start.

In this course, you will learn how to build deep learning models with PyTorch and Python. The course makes PyTorch a bit more approachable for people starting out with deep learning and neural networks.

Show More

What Will You Learn?

  • You will understand how to build deep learning models with PyTorch and Python.
  • The course makes PyTorch a bit more approachable for people starting out with deep learning and neural networks.

Course Content

Introduction

  • Introduction
    03:26

PyTorch Basics & Linear Regression

Image Classification with Logistic Regression

Training Deep Neural Networks on a GPU with PyTorch

Image Classification using Convolutional Neural Networks

Residual Networks, Data Augmentation and Regularization

Training Generative Adverserial Networks (GANs)