Deep Learning Essentials
Learn the foundations of deep learning and practice with training and implementing neural networks in Pytorch while covering topics including convolutional neural networks (CNNs), transformers, and generative adversarial networks (GANs).
Course taught by expert instructors
PhD at Stanford; formerly at Waymo, Microsoft
Kevin Wu, like his brother Eric, is a Ph.D. student at Stanford University. Previously, he spent time building deep learning products at a startup and has worked for Microsoft and Waymo. His current research focuses on applying AI to areas like clinical trials and genomics, as well as studying the generalizability of medical AI algorithms used in practice.
PhD at Stanford, formerly at Google
Eric Wu is a Ph.D. student at Stanford University, where he has published in major journals and conferences at the intersection of deep learning and health. Previously, he studied at Harvard and Duke University, where he taught introductory data science and deep learning courses, and worked at Google and DeepHealth, a health tech start up.
Learn and apply skills with real-world projects.
- ProjectSolve famous optimization functions: unleash the power of neural networks on mathematics!Learn
- What's special about deep learning?
- What makes neural networks good ML models?
- Introduce MLPs (Multi-Layer Perceptrons)
- ProjectTrain a neural network to recognize emojis from quick sketches! Also learn how to use Pytorch to implement your own neural network training pipeline.Learn
- Gradient descent
- Loss functions
- ProjectUse CNNs to improve your sketch to emoji app and create your own face style filter by fine-tune a StyleGANLearn
- Convolutional Neural Networks (CNNs)
- Generative Adversarial Networks (GANs)
- ProjectCreate a sentiment analysis tool to turn text messages into emojis and create a speech bot using Large Language ModelsLearn
- RNNs (recurrent neural networks)
- Large Language Models (LLMs)
Work on projects that bring your learning to life.
Made to be directly applicable in your work.
Live access to experts
Sessions and Q&As with our expert instructors, along with real-world projects.
Network & community
Core reviews a study groups. Share experiences and learn alongside a global network of professionals.
Support & accountability
We have a system in place to make sure you complete the course, and to help nudge you along the way.
Course success stories
Learn together and share experiences with other industry professionals
Eric and Kevin are exceptional at being able to distill complicated subjects into easy-to-understand insights. I usually find learning subjects online to be dry and confusing, but found their teaching style to be exciting and intuitive. I would highly recommend taking this course with them.
Reasoning about the ins and outs of deep learning has been super important for my work as a machine learning engineer. Eric and Kevin really know their stuff and can explain it with contagious enthusiasm. You’ll really enjoy their course!
As a product manager at a computer-vision based start-up, I’m constantly thinking through what deep learning can (or can’t) do for my team. Eric and Kevin have been able to explain emerging research fields in digestible ways every time. Their previous experience at big tech companies like Google and Waymo also helps them speak a common “tech” language that you don’t always get with folks in academia. This course will help you in your career, whether you are working with deep learning at a high level or want to become a machine learning engineer.
Kevin and Eric are passionate machine learning experts who also happen to be great storytellers. During our time at Harvard, I’ve sought them out many times for deep learning advice, and they’ve always been very helpful in explaining cutting edge concepts with ease. I would definitely recommend anyone who has an interest in deep learning to take their course!
I'm amazed by the deep learning skills I've learned in just 4 weeks. Eric and Kevin are extremely knowledgable about the theory and application of deep learning, and really know how to explain it in a way that makes sense to anyone. I felt fully supported in my learning journey and appreciated being surrounded by other motivated peers. This course is already making a huge impact in my career as I transition into a new role as a machine learning engineer. Highly recommend this course for anyone who wants to dive into deep learning!
It was a privilege to learn from Kevin and Eric! Through their lectures, assignments and one-on-one feedback, I was able to learn deep-learning concepts that were foreign to me. They truly cared for the learning and development of each student in their class. Thank you so much!
The Deep Learning Essentials course was a fantastic course for generalist software developers like me who are interested in getting their hands dirty with deep learning. The class goes from covering the basics of network training to having you fine-tune state of the art foundation models with applications in computer vision and NLP. What I loved most about the class is that it gives you the vocabulary, experience, and confidence to either collaborate more closely with ML engineers in your company or pivot into an ML role yourself.
Really good contents on this course : a general overview during the first week, then real concrete applications and programming through the following weeks! Gave me more desire to go deeper in the DL field.
This course is for...
Software engineers looking to focus on the most important deep learning topics and fundamentals
Anyone who want hands-on experience with deploying and training deep learning models.
Technically-minded individuals wanting to gain a practical intuition for current deep learning development and future progress.
In this course, we will be using Python, the dominant programming language for deep learning. A lot of the content in this course will require you to implement functions and code and will assume that you are comfortable reading and writing in Python. If you have questions about your knowledge in Python, feel free to reach out to the course staff, or consult the official Python tutorial to brush up on syntax.
Having some background in calculus, statistics, and linear algebra is also a plus. However, don't worry if you don't remember everything from that one calculus class you took in high school! We won't assume any specific knowledge, though concepts like derivatives and matrices will be touched on.
Finally, in our first week, we will have a quick primer on the basics of machine learning. If you've already had experience with machine learning before, great! Feel free to move as fast or slow as you find useful through this section. We will also include an abundance of resources beyond the scope of this class if you want to shore up your knowledge in this area in parallel.