Machine Learning 10 - Differentiable Programming | Stanford CS221: AI (Autumn 2021)

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai

Associate Professor Percy Liang
Associate Professor of Computer Science and Statistics (courtesy)
https://profiles.stanford.edu/percy-liang

Assistant Professor Dorsa Sadigh
Assistant Professor in the Computer Science Department & Electrical Engineering Department
https://profiles.stanford.edu/dorsa-sadigh

To follow along with the course schedule and syllabus, visit:
https://stanford-cs221.github.....io/autumn2021/#sched

0:00 Introduction
0:06 Machine learning: differentiable programming
0:47 Deep learning models
1:24 Feedforward neural networks
4:23 Representing images
5:18 Convolutional neural networks
10:29 Representing natural language
11:51 Embedding tokens
13:01 Representing sequences
14:17 Recurrent neural networks
17:38 Collapsing to a single vector
19:33 Long-range dependencies
19:59 Attention mechanism
26:17 Layer normalization and residual connections
28:38 Transformer
31:30 Generating tokens
32:36 Generating sequences
33:46 Sequence-to-sequence models
35:22 Summary FeedForward Conv MaxPool

#artificialintelligence #machinelearning