Introduction to Deep Learning with PyTorch Data Camp

Overview

Context

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. Discover this powerful technology and learn how to leverage it using PyTorch, one of the most popular deep learning libraries.

First, tackle the difference between deep learning and "classic" machine learning. In this Introduction to Deep Learning with PyTorch course offered by Data Camp you will learn about the training process of a neural network and how to write a training loop. 

In the second half of this course, you will learn 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.

What you'll learn

• Apply activation functions to introduce non-linearity in models
• Build and inspect tensors as the foundation of PyTorch models
• Construct and connect neural network layers
• Implement optimizer steps, scheduling, and training-loop housekeeping.
• Manage model modes, persistence, and parameter inspection.