Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 3 – Neural Networks

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3kzqrg1

Professor Christopher Manning
Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science
Director, Stanford Artificial Intelligence Laboratory (SAIL)

To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224n/...

0:00 Introduction
0:21 Course plan: coming up
2:02 Homeworks
6:49 Classification setup and notation
7:08 Classification intuition
8:52 Details of the softmax classifier
12:01 Background: What is "cross entropy" loss/error?
16:56 Traditional ML optimization
19:01 Neural Network Classifiers • Softmax le logistic regression alone not very powerful
22:43 Classification difference with word vectors
26:10 Neural computation
27:16 An artificial neuron
28:49 A neuron can be a binary logistic regression unit
35:17 Matrix notation for a layer
46:34 Binary word window classification
48:08 Window classification: Softmax
51:10 Neural Network Feed-forward Computation
55:08 Computing Gradients by Hand
59:44 Jacobian Matrix: Generalization of the Gradient

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