Deep Learning


San José State University
CS256: Topics in Artificial Intelligence
Spring 2018
Contact: guha.jayachandran@sjsu.edu (address is now working)
Class Days/Time: M/W 9-10:15AM
Classroom: DH 450
Office Hours: M/W 8:45-9AM in DH 450; also from 10:15AM (let the lecturer know at the end of class and office hours can continue in DH 282)
Tentative Schedule
Syllabus
Deep learning has revolutionized several problem domains in recent years. In this course, we will start with the fundamentals of neural networks and theory, and then proceed to look at several applications, including in image recognition, audio processing, and natural language processing. We will cover sequence data, recurrent neural networks, generative models, and reinforcement learning.

Lecture Materials

Lecture 1 slides
Lecture 2 (on Github)
Lecture 3 slides
Lecture 4 slides
Lecture 5 slides
Lecture 6 slides
Lecture 7 slides, Lecture 7, IPython NB
Lecture 8 slides
Lecture 9 slides
Deep Chemistry Guest Lecture
Lecture 10 slides
Lecture 11 slides
Lecture 12 slides
Lecture 13 slides
Lecture 14 slides
Lecture 15 slides, MNIST LSTM Jupyter Notebook
Lecture 16 slides, Seq2Seq Demo Notebook
Lecture 17 slides
Lecture 18 slides
Lecture 19 slides
Lecture 20 slides
Lecture 21 slides
Lecture 22 slides
Lecture 23 slides
Lecture 24 slides
Lecture 25 slides, Cart-Pole demo

Final Project Deliverables

Update: The poster is due on 05/07 but you can turn in your write-up as late as 05/14
Deliverables

Assignments

Assignment 1: Instructions | Dataset | Solutions
Assignment 2: Instructions | Starter Code
Assignment 3: Instructions | Starter Code

Speaking Schedule