Deep Learning Essentials
4 weeks US$ 400
Not ready? for new sessions.
Space is limited
Course logo

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).

Instructor profile photo
Kevin Wu
PhD at Stanford; formerly at Waymo, Microsoft
Instructor profile photo
Eric Wu
PhD at Stanford, formerly at Google
Price
US$ 400
Course Duration
4 weeks
Start Date
October 10
Registration By
October 7
Project Session
Wednesday @ 4:00 PM UTC
Learn alongside a small group of your professional peers
Connect with experts through live sessions and office hours
Real-world projects that teach you industry skills.
Created and taught by
Instructor Photo
Link to an instructor's LinkedIn profile

Kevin Wu

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.

Affiliation logo
Instructor Photo
Link to an instructor's LinkedIn profile

Eric Wu

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.

Affiliation logo
The course

Deep Learning is quickly becoming ubiquitous in our everyday lives – from our phone cameras to medical decision-making to self-driving cars. Understanding and knowing how to use deep learning is therefore an incredibly useful skill set to have, whether we are using existing neural networks or implementing our own to solve important problems in our work.

This course covers the foundations of deep learning and provides students with opportunities to practice training and using neural networks. Assuming no prior background in deep learning, we will use PyTorch to create an object sketch recognizer, a sentiment analysis tool, and a conversation bot. Topics will include convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and generative adversarial networks (GANs).

After taking this class, you will understand where and when to use deep learning and how to train and use neural networks. You will also have the skill set to expand your knowledge of deep learning, and to keep pace with state-of-the-art advances.

1
Week 1
Intro to Neural Networks
Learn
  • What's special about deep learning?
  • What makes neural networks good ML models?
  • Introduce MLPs (Multi-Layer Perceptrons)
Project
Solve famous optimization functions: unleash the power of neural networks on mathematics!
2
Week 2
Training Neural Networks
Learn
  • Gradient descent
  • Backpropagation
  • Loss functions
  • Optimizers
  • Metrics
Project
Train a neural network to recognize emojis from quick sketches! Also learn how to use Pytorch to implement your own neural network training pipeline.
3
Week 3
Vision
Learn
  • Convolutional Neural Networks (CNNs)
  • Generative Adversarial Networks (GANs)
Project
Use CNNs to improve your sketch to emoji app and create your own face style filter by fine-tune a StyleGAN
    4
    Week 4
    Text
    Learn
    • RNNs (recurrent neural networks)
    • Transformers
    • Large Language Models (LLMs)
    Project
    Create a sentiment analysis tool to turn text messages into emojis and create a speech bot using Large Language Models
      Dan ChernProduct Manager @ Facebook

      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.

      Joshua PhamMachine Learning Engineer @ Spotify

      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!

      Dhruv MaheshwariSenior Product Manager @ KeepTruckin, former Google PM

      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.

      Tommy Pan FangIncoming professor at Rice University, Harvard Business School PhD

      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!

      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.

      Prerequisites

      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.

      Course experience

      Live Sessions with Experts

      Top industry leaders teach you everything you need in only 4 weeks

      Interactive Learning

      Real-world projects put your learning into immediate action

      Professional Communities

      Grow your network by learning with an intimate cohort of peers from top companies
      ?
      Frequently Asked Questions
      Stay in the loop
      Keep in touch for updates, discounts, and new course sessions.
      Backed by top VCs, including
      VCs
      Teach
      Share your unique expertise with the world.
      Enterprise
      Receive best-in-class skills training for your teams and organization.
      Careers
      Join us as we change the future of online education.
      © 2021 - 2022 Corise Education. Terms of Service. Privacy Policy.
      Questions? Email us at hello@corise.com