Course Description Deep Learning is one of the most highly sought after skills in AI. We will help you become good at Deep Learning. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach. After this course, you will likely find creative ways to apply it to your work. This class is taught in the flipped-classroom format. You will watch videos and complete in-depth programming assignments and online quizzes at home, then come to class for advanced discussions and work on projects. This class will culminate in an open-ended final project, which the teaching team will help you on.
You’ll learn the technical details from linear regression to CNN, RNN and GAN (what my final project is on). You’ll also learn various efficient training, optimization, regularization and error analysis techniques. But what sets CS230 apart from other machine or deep learning courses is that it also brings a fresh industry perspective - how to structure a machine learning project from scratch, how to effectively collect and massage data, and how to evaluate a model so that the team can shoot for the right target and iterate faster. You really get to have hands on the state-of-the-art deep learning methods and fun applications. And of course Andrew’s exclusive academic and career advice!
- Weini Yu, Masters student in Computer Science
1. Even though I had already seen much of the material before (from other classes), I realized coming in that I still lacked intuition for actually implementing and designing deep learning systems. I haven't taken another AI/ML class yet that has provided so much good and practical advice.
2. GANs, GANs, GANs. I appreciate how GANs were covered so early on (week 4!), as it really demystified them for me. I had been aware that training GANs was tricky, but working with them for the final project has been extremely rewarding.
- Mark Sabini, Undergraduate student in Computer Science
This class has been so well run and I've had a lot of fun learning. My favorite part has been the class project and the weekly feedback and guidance during the TA meetings!
- Gili Rusak, Undergraduate student in Computer Science
This is the first time CS has really felt like engineering to me. I've found it really fun to feel like I'm building things, making design choices like I would if I were building a physical object. I feel like the class is organized really well in this way - it gives you the tools to be able to approach any general problem and come up with multiple ideas regarding how the pieces will fit together.
- Christine Tataru, Masters student in Computer Science
In the first Lecture, Andrew explains how AI in agriculture helps classifying infected cabbages from the healthy ones and eradicate them. He explains errors in this case might not have a huge impact for us, but it is a life or death situation for a cabbage :D Student's Inference: Well trained Deep learning model with higher accuracy can save many innocent cabbage lives.
- Karthik Selvakumar, Stanford Center for Professional Development (SCPD) student
Acknowledegment This webpage is using the code from Shuqui Qu and Ziang Xie who have built the CS229 webpage, special thanks to them.