Introduction to Deep Learning with PyTorch

Overview

Learn how to build your first neural network, adjust hyperparameters, and tackle classification and regression problems in PyTorch.

Deep learning is everywhere, from smartphone cameras to voice assistants or self-driving cars. In this course, you will discover this powerful technology and learn how to leverage it using PyTorch, one of the most popular deep learning libraries. By the end of this course, you will be able to leverage PyTorch to solve classification and regression problems on both tabular and image data using deep learning.

Syllabus

Introduction to PyTorch, a Deep Learning Library

Self-driving cars, smartphones, search engines... Deep learning is now everywhere. Before you begin building complex models, you will become familiar with PyTorch, a deep learning framework. You will learn how to manipulate tensors, create PyTorch data structures, and build your first neural network in PyTorch.

Training Our First Neural Network with PyTorch

To train a neural network in PyTorch, you will first need to understand the job of a loss function. You will then realize that training a network requires minimizing that loss function, which is done by calculating gradients. You will learn how to use these gradients to update your model's parameters, and finally, you will write your first training loop.

Neural Network Architecture and Hyperparameters

Hyperparameters are parameters, often chosen by the user, that control model training. The type of activation function, the number of layers in the model, and the learning rate are all hyperparameters of neural network training. Together, we will discover the most critical hyperparameters of a neural network and how to modify them.

Evaluating and Improving Models

Training a deep learning model is an art, and to make sure our model is trained correctly, we need to keep track of certain metrics during training, such as the loss or the accuracy. We will learn how to calculate such metrics and how to reduce overfitting using an image dataset as an example.