Real-world projects from industry experts
With real-world projects and immersive content built in partnership with top-tier companies, you’ll master the tech skills companies want.
Deep Learning
Nanodegree Program
Learn to leverage the capabilities of deep learning tools to fix complex problems and unlock next-level results for enterprises.
04Days08Hrs49Min18Sec
Estimated time
4 months
At 10 hours/week
Enroll by
November 30, 2022
Get access to classroom immediately on enrollment
Prerequisites
Intermediate Python
What you will learn
Deep Learning
4 months to completeJoin the next generation of deep learning talent that will help define a highly beneficial AI-powered future for our world. In this program, you’ll study cutting-edge topics such as neural networks, convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Prerequisite knowledge
Introduction to Deep Learning
Begin by learning the fundamentals of deep learning. Then examine the foundational algorithms underpinning modern deep learning: gradient descent and backpropagation. Once those foundations are established, explore design constructs of neural networks and the impact of these design decisions. Finally, the course explores how neural network training can be optimized for accuracy and robustness.
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 better than standard neural networks for image processing. Then you’ll examine the inner workings of CNNs and apply the architectures to custom datasets using transfer learning. Finally, you will learn how to use CNNs for object detection and semantic segmentation.
RNNs & Transformers
This course covers multiple RNN architectures and discusses design patterns for those models. Additionally, you’ll focus on the latest transformer architectures.
Building Generative Adversarial Networks
Become familiar with generative adversarial networks (GANs) by learning how to build and train different GANs architectures to generate new images. Discover, build, and train architectures such as DCGAN, CycleGAN, ProGAN, and StyleGAN on diverse datasets including the MNIST dataset, Summer2Winter Yosemite dataset, or CelebA dataset.
All our programs include:
Technical mentor support
Our knowledgeable mentors guide your learning and are focused on answering your questions, motivating you, and keeping you on track.
Career services
You’ll have access to Github portfolio review and LinkedIn profile optimization to help you advance your career and land a high-paying role.
Flexible learning program
Tailor a learning plan that fits your busy life. Learn at your own pace and reach your personal goals on the schedule that works best for you.
Program offerings
Class Content
- Real-world projects
- Project reviews
- Project feedback from experienced reviewers
Student services
- Technical mentor support
- Student community
Career services
- Github review
- Linkedin profile optimization
Succeed with personalized services.
We provide services customized for your needs at every step of your learning journey to ensure your success.
- Experienced Project Reviewers
- Technical Mentor Support
Get timely feedback on your projects.
- Personalized feedback
- Unlimited submissions and feedback loops
- Practical tips and industry best practices
- Additional suggested resources to improve
1,400+ project reviewers
2.7M projects reviewed
88/100 reviewer rating
1.1 hours avg project review turnaround time
Learn with the best.
Learn with the best.
Erick Galinkin
Principal AI Researcher | Rapid7Erick 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.
Giacomo Vianello
Principal Data ScientistGiacomo 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.
Nathan Klarer
Head of ML & COO of DatyraNathan 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.
Thomas Hossler
Sr Deep Learning EngineerThomas 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.
Deep Learning
Get started today
Monthly access
Pay as you go
Enroll now
per/
/- Maximum flexibility to learn at your own pace.
- Cancel anytime.
- access
Pay upfront and save an extra 0%
Enroll now
for - access- Save an extra 0% vs. pay as you go.
- 4 months is the average time to complete this course.
- Switch to monthly price after if more time is needed.
- Cancel anytime.
Best Value
Program details
Program overview: Why should I take this program?
Why should I enroll?
What jobs will this program prepare me for?
How do I know if this program is right for me?
Enrollment and admission
Do I need to apply? What are the admission criteria?
What are the prerequisites for enrollment?
If I do not meet the requirements to enroll, what should I do?
Tuition and term of program
How is this Nanodegree program structured?
How long is this Nanodegree program?
Can I switch my start date? Can I get a refund?
I have graduated from the Deep Learning Nanodegree program but I want to keep learning. Where should I go from here?
Software and hardware: What do I need for this program?
What software and versions will I need in this program?