# New? Start Here

You can see the one-paragraph description of the blog in the footers, repeated here for convenience:

*This is my blog, where I have written over 250 articles on a variety of topics,
most of which are about one of two major themes. The first is computer science,
which is my area of specialty as a Ph.D. student at UC Berkeley. The second can
be broadly categorized as “deafness,” which relates to my experience and
knowledge of being deaf.*

The easiest way to find something that you’ll be interested in is to look at the archives and browse the titles, which (I hope) are descriptive. Using the built-in Google site search there would also be useful.

If you’re interested in knowing more about the classes at Berkeley, I write reviews on all the ones I have taken. Here they are:

- CS 267, Applications of Parallel Computing
- CS 280, Computer Vision
- CS 281A, Statistical Learning Theory
- CS 287, Advanced Robotics
- CS 288, Natural Language Processing
- CS 294-112, Deep Reinforcement Learning
- CS 294-112, Deep Reinforcement Learning (self-study)
- CS 294-115, Algorithmic Human-Robot Interaction
- CS 294-129, Deep Neural Networks (GSI/TA)
- EE 227BT, Convex Optimization
- EE 227C, Convex Optimization and Approximation
- STAT 210A, Theoretical Statistics
- STAT 210B, Theoretical Statistics

When I was preparing for the AI prelims at Berkeley, I wrote a lot about AI topics. I also wrote a “transcript” of my prelims.

- My Prelims
*[Transcript]* - Miscellaneous Prelim Review (Part 1)
- Miscellaneous Prelim Review (Part 2)
- Markov Decision Processes and Reinforcement Learning
- Perceptrons, SVMs, and Kernel Methods
- Notes on Exact Inference in Graphical Models
- Closing Thoughts on Graphical Models
- The Least Mean Squares Algorithm
- Expectation-Maximization
- Hidden Markov Models and Particle Filtering
- Reading Russell and Norvig
- Stanford’s Linear Algebra Review

I also write a lot about other technical areas, and am attempting to write up more about my thoughts on various technical research papers. Here are a few:

- Mathematical Tricks Commonly Used in Machine Learning and Statistics
- Notes on the Generalized Advantage Estimation Paper
- Going Deeper Into Reinforcement Learning: Fundamentals of Policy Gradients
- Going Deeper Into Reinforcement Learning: Understanding Deep-Q-Networks
- Going Deeper Into Reinforcement Learning: Understanding Q-Learning and Linear Function Approximation
- Understanding Higher Order Local Gradient Computation for Backpropagation in Deep Neural Networks
- Understanding Generative Adversarial Networks
- Some Recent Results on Minibatch Markov Chain Monte Carlo Methods
- Independent Component Analysis — A Gentle Introduction
- Ten Things Python Programmers Should Know

If you are interested in knowing about what it’s like being deaf, then there are
a *lot* of options. Here are a few that might be informative:

- The Obligatory “Can I Lip Read?” Question
- The BVLC (BAIR) Retreat: Disaster Averted!
- Advocate for Yourself
- After a Few Weeks of CART, Why do I Feel Dissatisfied?
- The Problem with Seminars
- My Pre-College Education as a Deaf Mainstreamed Student
- New Closed-Captioning Glasses
- Hearing Aids: How They Help and How They Fall Short in Group Situations
- Technical Term Dilemma
- Why Computer Science is a Good Major for Deaf Students

Finally, I write sometimes about the books I read, such as in the following: