Deep Learning

An Introduction to Your Nanodegree Program

Welcome! We're so glad you're here. Join us in learning a bit more about what to expect and ways to succeed.

Getting Help

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

Introduction to Deep Learning

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

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.

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.

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.

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!

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.

Introduction to CNNs

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.

CNN Concepts

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.

CNNs in Depth

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.

Transfer Learning

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.

Autoencoders

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

Object Detection and Segmentation

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.

Landmark Classification & Tagging for Social Media

In this project, you will apply the skills you have acquired in the Convolutional Neural Network (CNN) course to build a landmark classifier.

Intro to RNN

Introduction to LSTM

Introduction to Transformers

Text Translation and Sentiment Analysis using Transformers

Introduction to Generative Adversarial Networks

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

Generative Adversarial Networks

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

Training a Deep Convolutional GANs

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

Image to Image Translation

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.

Modern GANs

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

Face Generation

Define two adversarial networks, a generator, and a discriminator, and train them until you can generate realistic faces.

Take 30 Min to Improve your LinkedIn

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Optimize Your GitHub Profile

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Congratulations!

Congratulations on your graduation from this program! Please join us in celebrating your accomplishments.