Introduction to Deep Learning

Course

This course covers foundational deep learning theory and practice. We begin with how to think about deep learning and when it is the right tool to use. The course covers the fundamental algorithms of deep learning, deep learning architecture and goals, and interweaves the theory with implementation in PyTorch.

Intermediate
4 weeks
Last Updated November 5, 2024

Skills you'll learn:

Model performance metricsPerceptron

Prerequisites:

Linear algebra

Intermediate

4 weeks

Last Updated November 5, 2024

Skills you'll learn:

Model performance metrics • Perceptron • Neural networks • Deep learning

Prerequisites:

Linear algebra

Course Lessons

Lesson 1

Introduction to Deep Learning

Meet your instructor, get an overview of the course, and find a few interesting resources in this introductory lesson.

Lesson 2

Deep Learning

This introductory lesson on Deep Learning covers how experts think about deep learning and how to know when deep learning is the right tool for the job, including some examples.

Lesson 3

Minimizing Error Function with Gradient Descent

Beginning with PyTorch and moving into both Error Functions, Gradient Descent, and Backpropagation, this lesson provides an overview of foundational neural network concepts.

Lesson 4

Intro to Neural Networks

This introduction to neural networks explains how algorithms inspired by the human brain operate and puts to use those concepts when designing a neural network to solve particular problems.

Lesson 5

Training Neural Networks

Learn how to train neural networks and avoid overfitting or underfitting by employing techniques like Early Stopping, Regularization, Dropout, Local Minima, and Random Restart!

Lesson 6 • Project

Developing a Handwritten Digits Classifier with PyTorch

In this project, you will use your skills in designing and training neural networks to classify handwritten digits using the well-known MNIST dataset.

Taught By The Best

Erick Galinkin

Principal AI Researcher

Erick Galinkin is a hacker and computer scientist, leading research at the intersection of security and artificial intelligence at Rapid7. He has spoken at numerous industry and academic conferences on topics ranging from malware development to game theory in security.

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