Announcements

Under-planning

• Paper discussion seminars
• PyTorch Workshop for General ACM Members (TBD, not sure if it's happening or not)
• Aim for Spring EOH Projects

Meeting Summaries

First Meeting -- 9/7

• Welcome icebreaker.
• Split up into Vision/NLP/RL groups to discuss plans for the semester.
• Some people decided to work on Stanford's Computer Vision (CS231n), Berkeley's Reinforcement Learning (CS 294).

Second Meeting -- 9/14

• Split into groups for discussion about various projects, targeting Spring 2017 Open House
• RL Group went over Berkeley's CS 294 on Sunday (8/17)
• Dominic talked to the Computer Vision Group about his research

Third Meeting -- 9/14

• Computer Vision began to learn Tensorflow, having a workshop on Thursday (8/21)
• Reinforcement Learning Group is having a PyTorch Workshop/Lecture, replacing CS 294's Tensorflow Lecture
• NLP has began to talk about what projects to potentiall work on (speech synthesis/chat bot)

Tensorflow Workshop -- 9/21

• Note the presentation had a bug with LeNet. Both the slides and examples have been updated.
• Presentation GitHub
• Presentation Slides
• Covered the following topics:
  • Tensorflow Session, Variables, Placeholders, Graphs, and Ops.
  • Supervised Learning Review
  • Typical Loss Functions
  • Linear Regression/Classification
  • Implementing LeNet

Fourth Meeting -- 9/25

• Meeting Slides
• Display feedback results -- hopefully we have 1111 this time.
• Tensorflow workshop review.
• Will gave a presentation about his research (trains and using inverse reinforcement learning to derive a human-like reward function).
• Dominic gave a presentation about SSD (Single Shot Detectors).
• NLP Group will be giving assignments to members, as a start to their project

Fifth Meeting -- 10/02

• Meeting Slides
• Brief Discussion about the Intergroup Discussions planned for around November
• Some points brought up (in addition to the slides)
  • People need prior knowledge to understand some of the papers, prereqs should be listed somewhere
  • Most people don't know how to read papers, we'll need a good split on the people that can actually read vs. people that can.
• RL Group Covered:
  • Value Function and on-policy/off-policy review.
  • Covered Q-Learning, SARSA, Double Q-Learning.
  • Advantages and Disadvantages of on/off policy
  • Some intuition on how to actually program Q-Learning. (Small introduction on epsilon greedy, mentioned Experience Replay, Hyperparameter Selection, all the gross stuff)
• NLP Group Covered:
  • Basic Models