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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

talks

teaching

Efficient Algorithms and Intractable Problems (CS 170)

Undergraduate course, UC Berkeley, EECS, 1900

Teaches core algorithmic concepts in Computer Science, including Divide-and-Conquer, Graph, Greedy Algorithms, Dynamic Programming, Linear Programming, Reductions, P vs. NP, Streaming, etc.

Neural Networks (CS 342)

Undergraduate course, UT Austin, CS, 1900

This course covers the basic building blocks and intuitions behind designing, training, tuning, and monitoring of deep networks. It covers both the theory of deep learning, as well as hands-on implementation sessions in pytorch. It also covers a series of application areas of deep networks in: computer vision, sequence modeling in natural language processing, deep reinforcement learning, generative modeling, and adversarial learning.

Artificial Intelligence (CS 343)

Undergraduate course, UT Austin, CS, 1900

This course provides a broad introduction to artificial intelligence. Topics include: problem solving, including search and game playing; knowledge and reasoning, including inference planning; reasoning under uncertainty; machine learning