@ameobia10· completed
this was a cool read that detailed a first-time open source contributors pr process. but more than that @ameobia10 shared the thought process behind finding an issue, grabbing the codebase, navigating the codebase, reproducing the issue, writing code and tests, and going through the cl (chromium pr) process. then the rewarding parts of seeing it in chrome canary 128 which he actively uses.
Sergey Ioffe, et al.· soon
...
Jean Mary John, et al.· in-progress
related to research work @ traderspost
Yingze Liu· in-progress
related to research work @ traderspost
Anna Kolomiiets, et al.· in-progress
related to research work @ traderspost
Saran Kumar, et al.· in-progress
related to research work @ traderspost
David M. Blei, et al.· completed
getting into the weeds of variational inference (vi). the book does a good job explaining that vi is not just a bayesian tool but also has many other applications like reinforcement learning. the concepts were mostly new to me as i mainly studied mcmc during classes. vi is clearly a powerful tool for approximating posterior distributions in a optimization framework. it would be cool to go and replicate something similar to the nytimes probabilistic topic modeling example.
Diederik P. Kingma, et al.· completed
starting to see some patterns. estimate the variational lower bound (stochastic gradient variational bayes in this case). then apply an efficient algorithm for inference (auto-encoding variational bayes). this paper had good derivations and clear experiment procedures for comparing wake sleep, mcem, and aevb algorithms.
Thomas L. Griffiths· completed
wow. great read. it smoothly goes from describing bayesian inference to using it in practice when dealing with complex distributions and problem types. also mentioned dynamic programming and graphs which made me happy since im doing a lot of dsa practice right now.
Joshua T. Tenenbaum, et al.· completed
theory-based bayesian thinking behind human cognitive science. paper highlights interactions of sophisticated inference processes or sophisticated konwldge representations. a mix of tree-structured priors, casual graphs, and the tendancy for smaller more specific hypothesis to be preferred over larger generalized ones.
Blaise Agüera y Arcas, et al.· completed
super early system design for a future space-based ai infrastructure. covers physical solar panel design, data transmission methods, cost estimatation based on spacex launch history, and tpu radiation testing. cool read.
Anthropic· completed
using llms every day is cool, but not being aware of the risks beyond a couple 'extremist' reels is not. i decided to read this to understand what anthropics views on ai safety and the future impact of ai. its actually insane how much effort is being put into research at anthropic. i personally find the ideas of mechanistic interpretability interesting, but i think i should align myself (no joke) with the alignment capabiliteis and alignment science research first since that is the core of the safety effort right now.
quantitative trading
Ernest P. Chan· completed
this book is high level for a good reason. it covers the practical implications of trading automated systems and scaling a portfolio. from data quality to risk management. when to start or end a business like this from someone who has done it in both a professional desk and home office.
structured computer organization
Andrew S. Tanenbaum· completed
a bottom-up walkthrough from digital logic to the OS layer. great visuals on microarchitecture. i enjoyed reading through the whole book in my free time but it would also be a good choice for referencing individual chapters.
a practical guide to quantitative finance interviews
Xinfeng Zhou· completed
the book did a good job tieing together the math and practical aspects of finance trading ie markov chains, risk, etc. a good read if you want to get a quick breadth refresher imo.
| Title | Author | Progress | Thoughts | Updated |
|---|---|---|---|---|
| fixing a bug in google chrome as a first-time contributor | @ameobia10 | completed | this was a cool read that detailed a first-time open source contributors pr process. but more than that @ameobia10 shared the thought process behind finding an issue, grabbing the codebase, navigating the codebase, reproducing the issue, writing code and tests, and going through the cl (chromium pr) process. then the rewarding parts of seeing it in chrome canary 128 which he actively uses. | 20260501 |
| deep residual learning for image recognition | Kaiming He, et al. | soon | ... | |
| batch normalization: accelerating deep network training by reducing internal covariate shift | Sergey Ioffe, et al. | soon | ... | |
| dropout: a simple way to prevent neural networks from overfitting | Nitish Srivastava, et al. | soon | ... | |
| a method for stochastic optimization | Diederik P. Kingma, et al. | soon | ... | |
| an exploration of clustering algorithms for customer segmentation in the uk retail market | Jean Mary John, et al. | in-progress | related to research work @ traderspost | |
| customer segmentation in user behavior analysis: a comparative study of clustering algorithms | Yingze Liu | in-progress | related to research work @ traderspost | |
| customer churn prediction in the software by subscription models it business using machine learning methods | Anna Kolomiiets, et al. | in-progress | related to research work @ traderspost | |
| a survey on customer churn prediction using machine learning techniques | Saran Kumar, et al. | in-progress | related to research work @ traderspost | |
| variational inference: a review for statisticians | David M. Blei, et al. | completed | getting into the weeds of variational inference (vi). the book does a good job explaining that vi is not just a bayesian tool but also has many other applications like reinforcement learning. the concepts were mostly new to me as i mainly studied mcmc during classes. vi is clearly a powerful tool for approximating posterior distributions in a optimization framework. it would be cool to go and replicate something similar to the nytimes probabilistic topic modeling example. | 20260428 |
| auto-encoding variational bayes | Diederik P. Kingma, et al. | completed | starting to see some patterns. estimate the variational lower bound (stochastic gradient variational bayes in this case). then apply an efficient algorithm for inference (auto-encoding variational bayes). this paper had good derivations and clear experiment procedures for comparing wake sleep, mcem, and aevb algorithms. | 20260429 |
| black box variational inference | Rajesh Ranganath, et al. | soon | ... | |
| a primer on probabilistic inference | Thomas L. Griffiths | completed | wow. great read. it smoothly goes from describing bayesian inference to using it in practice when dealing with complex distributions and problem types. also mentioned dynamic programming and graphs which made me happy since im doing a lot of dsa practice right now. | 20260426 |
| theory-based bayesian models of inductive learning and reasoning | Joshua T. Tenenbaum, et al. | completed | theory-based bayesian thinking behind human cognitive science. paper highlights interactions of sophisticated inference processes or sophisticated konwldge representations. a mix of tree-structured priors, casual graphs, and the tendancy for smaller more specific hypothesis to be preferred over larger generalized ones. | 20260426 |
| towards a future space-based, highly scalable ai infrastructure system design | Blaise Agüera y Arcas, et al. | completed | super early system design for a future space-based ai infrastructure. covers physical solar panel design, data transmission methods, cost estimatation based on spacex launch history, and tpu radiation testing. cool read. | 20260427 |
| core views on AI safety | Anthropic | completed | using llms every day is cool, but not being aware of the risks beyond a couple 'extremist' reels is not. i decided to read this to understand what anthropics views on ai safety and the future impact of ai. its actually insane how much effort is being put into research at anthropic. i personally find the ideas of mechanistic interpretability interesting, but i think i should align myself (no joke) with the alignment capabiliteis and alignment science research first since that is the core of the safety effort right now. | 20260421 |
| quantitative trading | Ernest P. Chan | completed | this book is high level for a good reason. it covers the practical implications of trading automated systems and scaling a portfolio. from data quality to risk management. when to start or end a business like this from someone who has done it in both a professional desk and home office. | 20260425 |
| structured computer organization | Andrew S. Tanenbaum | completed | a bottom-up walkthrough from digital logic to the OS layer. great visuals on microarchitecture. i enjoyed reading through the whole book in my free time but it would also be a good choice for referencing individual chapters. | 20260425 |
| a practical guide to quantitative finance interviews | Xinfeng Zhou | completed | the book did a good job tieing together the math and practical aspects of finance trading ie markov chains, risk, etc. a good read if you want to get a quick breadth refresher imo. | 20260425 |