Deep Learning

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

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.

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

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.

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

In this lesson we will look at the main applications of CNNs, understand professional roles involved in the development of a CNN-based application, and learn about the history of CNNs.

In this lesson we will recap how to use a Multi-Layer Perceptron for image classification, understand the limitations of this approach, and learn how CNNs can overcome these limitations.

In this lesson we will study in depth the basic layers used in CNNs, build a CNN from scratch in PyTorch, use it to classify images, improve its performance, and export it for production.

In this lesson we will learn about key CNN architectures and their innovations, and apply multiple ways of adapting them to our use cases with transfer learning.

In this lesson we will design and train linear and CNN-based autoencoders for anomaly detection and for image denoising.

In this lesson we will study applications of CNNs beyond image classification. We will train and evaluate an object detection model as well as a semantic segmentation model on custom datasets.

Introduction to this course, prerequisites, and your course instructor.

Ian Goodfellow, the inventor of GANs, introduces you to these exciting models. You'll also implement your own GAN on the MNIST dataset.

In this lesson, you'll implement a Deep Convolution GAN to generate complex color images.

Jun-Yan Zhu, one of the creators of the CycleGAN, will lead you through Pix2Pix and CycleGAN formulations that learn to do image-to-image translation tasks.

In this lesson, you will implement more advanced GAN architectural techniques that have had a significant impact on the realism of generated images.

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