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danieltakeshi.github.io

Seita's Place

This is my blog, where I have written over 225 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

http://danieltakeshi.github.io/

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Seita's Place | danieltakeshi.github.io Reviews

https://danieltakeshi.github.io

This is my blog, where I have written over 225 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

INTERNAL PAGES

danieltakeshi.github.io danieltakeshi.github.io
1

Going Deeper Into Reinforcement Learning: Understanding Deep-Q-Networks

https://danieltakeshi.github.io/2016/12/01/going-deeper-into-reinforcement-learning-understanding-dqn

Going Deeper Into Reinforcement Learning: Understanding Deep-Q-Networks. Dec 1, 2016. The Deep Q-Network (DQN) algorithm, as introduced by DeepMind in a NIPS 2013 workshop paper, and later published in Nature 2015 can be credited with revolutionizing reinforcement learning. In this post, therefore, I would like to give a guide to a subset of the DQN algorithm. This is a continuation of an earlier reinforcement learning article. About the preprocessing steps for game frames. Huh, interesting) with some so...

2

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

https://danieltakeshi.github.io/2016-05-31-the-master-algorithm-how-the-quest-for-the-ultimate-learning-machine-will-remake-our-world

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. May 31, 2016. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. By acclaimed computer science professor Pedro Domingos. Of the University of Washington. Or, as Domingos humorously puts it,. One algorithm to rule them all. I also think his attempts at explaining the master algorithm at the end fall prey to excessive storytelling. After his long, adventure-related meta...

3

Islam and the Future of Tolerance

https://danieltakeshi.github.io/2016-06-18-islam-and-the-future-of-tolerance

Islam and the Future of Tolerance. Jun 18, 2016. Islam and the Future of Tolerance: A Dialogue. By renowned atheist Sam Harris. And former jihadist-turned-liberal-activist Maajid Nawaz. I had this book on my radar for a while, and decided to read it yesterday in the wake of the tragic news of the recent Orlando shootings. My hope is to better understand what drives people to extremism and how we can reduce the probability of similar attacks from occurring. To respond to controversy. As of this writing,.

4

GSI-ing for Berkeley's Deep Neural Networks Class

https://danieltakeshi.github.io/2016-09-24-gsi-for-berkeleys-deep-neural-networks-class

GSI-ing for Berkeley's Deep Neural Networks Class. Sep 24, 2016. In news that might be welcome to the students taking CS 294-129, Designing, Visualizing, and Understanding Deep Neural Networks. This semester, I got recruited as the second GSI (Graduate Student Instructor, a.k.a. Teaching Assistant) for the class. Why do I say this might be good news for the students? Was one of those students for a brief time in undergrad (thankfully, since outgrown). Who wanted to take the class but. Same questions )&#4...

5

Frame Skipping and Pre-Processing for Deep Q-Networks on Atari 2600 Games

https://danieltakeshi.github.io/2016/11/25/frame-skipping-and-preprocessing-for-deep-q-networks-on-atari-2600-games

Frame Skipping and Pre-Processing for Deep Q-Networks on Atari 2600 Games. Nov 25, 2016. For at least a year, I’ve been a huge fan of the Deep Q-Network algorithm. It’s from Google DeepMind, and they used it to train AI agents to play classic Atari 2600 games at the level of a human. While only looking at the game pixels and the reward. In other words, the AI was learning just as we would do! To play the Atari 2600 games, we generally make use of the Arcade Learning Environment library. Of the screenshot...

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