Introduction to Deep Learning with PyTorch

Course Description

Introduction to Deep Learning with PyTorch

Deep learning is everywhere: in smartphone cameras, voice assistants, and self-driving cars. It has even helped discover protein structures and beat humans at the game of Go. 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.

Train your first neural network

First, this course tackles the difference between deep learning and “classic” machine learning and will introduce neural networks. You will learn about the training process of a neural network and how to write a training loop. To do so, you will create loss functions for regression and classification problems and leverage PyTorch to calculate their derivatives.

Evaluate and improve your model

In the second half of this course, you will learn about the different hyperparameters you can adjust to improve your model. After learning about the different components of a neural network, you will be able to create larger and more complex architectures. To measure your model performances, you will leverage TorchMetrics, a PyTorch library for model evaluation. 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.

What You’ll Learn

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.

Adjusting hyperparameters to improve performance

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.

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.

Training, Evaluating and Iterating

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.