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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