Learn the fundamentals of neural networks with Python and PyTorch, and then use your new skills to create your own image classifier—an application that will first train a deep learning model on a dataset of images and then use the trained model to classify new images.
Overview
Syllabus
- Course Introduction
- Meet your instructors, get a short overview of what you'll be learning, check your prerequisites, and learn how to use the workspaces and notebooks found throughout the lessons.
- Introduction to Neural Networks
- In this lesson, Luis will give you solid foundations on deep learning and neural networks. You'll also implement gradient descent and backpropagation in Python right here in the classroom.
- Implementing Gradient Descent
- Mat will introduce you to a different error function and guide you through implementing gradient descent using numpy matrix multiplication.
- Training Neural Networks
- Now that you know what neural networks are, in this lesson you will learn several techniques to improve their training.
- Deep Learning with PyTorch
- Learn how to use PyTorch for building deep learning models.
- Create Your Own Image Classifier
- In this project, you'll create your own image classifier and then train—and evaluate its performance—using one of the most classic and well-studied computer vision data sets, CIFAR-10.
Taught by
Luis Serrano, Mat Leonard and Erick Galinkin
