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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 graduate-level classes at Berkeley, I write reviews on all the ones I have taken. Here they are:

I also write a lot technical subjects. For example, here are some Generic Technical Guides, not including those related to my prelims studying (discussed later):

Ten Things Python Programmers Should Know (my most popular blog post)
Better Saving and Logging for Research Experiments
Adversarial Examples and Taxonomies
Basics of Bayesian Neural Networks
Independent Component Analysis — A Gentle Introduction
Mathematical Tricks Commonly Used in Machine Learning and Statistics
Going Deeper Into Reinforcement Learning: Fundamentals of Policy Gradients
Going Deeper Into Reinforcement Learning: Deep-Q-Networks
Going Deeper Into Reinforcement Learning: Q-Learning and Linear Function Approximation
Understanding Backpropagation Gradient Computation: post 1 and post 2
Domain Randomization Tips
Importance Sampling Review
Understanding Generative Adversarial Networks
Fundamentals of Robotics (SE, Twists, etc.): post 1 and post 2

Here are notes on Specific Research Papers (for others, see this GitHub repository), arranged roughly in order of topic similarity.

(Asynchronous) Advantage Actor-Critic
Prioritized Experience Replay
Notes on the Generalized Advantage Estimation Paper
Deep Q-learning from Demonstrations (DQfD)
DQfD Follow-ups (3 papers: Distributed PER, Ape-X, Kickstarting)
Off-Policy Deep Reinforcement Learning without Exploration
Learning to Teach
Born Again Neural Networks
Papers That Have Cited Policy Distillation (links to external PDF)
Interpretable and Pedagogical Examples
Model-Agnostic Meta Learning (MAML)
One-Shot Visual Imitation Learning via Meta-Learning
Zero-Shot Visual Imitation
Hindsight Experience Replay
Overcoming Exploration in Reinforcement Learning with Demonstrations
Learning to Act by Predicting the Future
Learning to Poke by Poking: Experiental Learning of Intuitive Physics
Self-Supervision Papers (7 papers, including some discussed separately)
Dense Object Descriptors with rope and fitting applications (4-5 papers)
Deep Learning for Robotic Grasping (3 papers)
Read-Through of Multi-Level Discovery of Deep Options
Understanding Deep Learning Requires Re-Thinking Generalization
Transformer Networks for State of the Art Natural Language Processing
Some Recent Results on Minibatch Markov Chain Monte Carlo Methods
Understanding and Categorizing Scalable MCMC and MH Papers (7 papers)
Minibatch Metropolis-Hastings (one of my papers!)

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

I later wrote a transcript of my qualifying exam.

I also blog about academic conferences. Here are the ones I have discussed, in chronological order and the city:

I sometimes make one post per day. For simplicity, the above will link to the first blog post for each of the conferences.

If you are interested in knowing about what it’s like being deaf then, while I obviously can’t claim to speak for everyone who has hearing impairments, here are a few that might be informative:

In addition, most of my posts on going to academic conferences discuss some of the above topics, as well as my prelims and quals transcripts.

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

I’m addicted to reading books. I’m happy to get book recommendations from you.