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Deep Learning

Nanodegree Program

This Nanodegree trains the learner about foundational topics in the exciting field of deep learning, the technology behind state-of-the-art artificial intelligence.

This Nanodegree trains the learner about foundational topics in the exciting field of deep learning, the technology behind state-of-the-art artificial intelligence.

Intermediate

4 months

Real-world Projects

Completion Certificate

Last Updated June 18, 2024

Skills you'll learn:
Generative adversarial networks • Model evaluation • Deep learning techniques • Markov games
Prerequisites:
Python proficiency • Pandas • Matrix multiplication

Courses In This Program

Course 1 45 minutes

Welcome to the Deep Learning Nanodegree Program

The Deep Learning Nanodegree program offers a solid introduction to the world of artificial intelligence. In this program, you’ll master fundamentals that will enable you to go further in the field, launch or advance a career, and join the next generation of deep learning talent that will help define a beneficial, new, AI-powered future for our world. You will study cutting-edge topics such as Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks, and build projects in PyTorch.

Lesson 1

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.

Lesson 2

Getting Help

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

Course 2 4 weeks

Introduction to Deep Learning

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.

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.

Course 3 4 weeks

Convolutional Neural Networks

This course introduces Convolutional Neural Networks, the most widely used type of neural networks specialized in image processing. You will learn the main characteristics of CNNs that make them so useful for image processing, their inner workings, and how to build them from scratch to complete image classification tasks. You will learn what are the most successful CNN architectures, and what are their main characteristics. You will apply these architectures to custom datasets using transfer learning. You will also learn about autoencoders, a very important architecture at the basis of many modern CNNs, and how to use them for anomaly detection as well as image denoising. Finally, you will learn how to use CNNs for object detection and semantic segmentation.

Lesson 1

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.

Lesson 2

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.

Lesson 3

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.

Lesson 4

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.

Lesson 5

Autoencoders

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

Lesson 6

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.

Lesson 7 • Project

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.

Course 4 4 weeks

RNNs and Transformers

This course covers multiple RNN architectures and discusses design patterns for those models. You'll also learn about transformer architectures.

Lesson 1

Intro to RNN

Lesson 2

Introduction to LSTM

Lesson 3

Introduction to Transformers

Lesson 4 • Project

Text Translation and Sentiment Analysis using Transformers

Taught By The Best

Photo of Giacomo Vianello

Giacomo Vianello

Principal Data Scientist

Giacomo Vianello is an end-to-end data scientist with a passion for state-of-the-art but practical technical solutions. He is Principal Data Scientist at Cape Analytics, where he develops AI systems to extract intelligence from geospatial imagery bringing, cutting-edge AI solutions to the insurance and real estate industries.

Photo of Nathan Klarer

Nathan Klarer

Head of ML & COO of Datyra

Nathan is a data scientist and entrepreneur. He currently leads a Datyra, a 50-person AI consultancy. He was the first AI team member at $CORZ. Prior to that he founded a VC backed data startup that was acquired. Nathan was named “27 CEO's Under 27” by Entrepreneur.com and has been featured in Inc. and Forbes.

Photo of Erick Galinkin

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.

Photo of Thomas Hossler

Thomas Hossler

Sr Deep Learning Engineer

Thomas is originally a geophysicist but his passion for Computer Vision led him to become a Deep Learning engineer at various startups. By creating online courses, he is hoping to make education more accessible. When he is not coding, Thomas can be found in the mountains skiing or climbing.

Ratings & Reviews

Average Rating: 4.7 Stars

909 Reviews

Gwanghyeon B.

January 20, 2023

생각보다 많은 것을 알려주고 어려웠지만 도움이 되었다고 생각합니다. 다만 딥러닝 영역이 광범위해서 후반부로 갈수록 초기 내용을 잊게 되네요

Abdulrahman A.

December 6, 2022

it was at first a bit confusing since each reviewer had different opinion but thanks for the good information i got

Loai M.

November 17, 2022

really good ,I did learn a lot

Emad E.

September 23, 2022

It's going great! It feels refreshing that the course is kept up-to-date with the current standards of deep learning techniques and architectures.

Shawky A.

September 21, 2022

good, but it's very hard to submit the project with this way

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