“There are some things which cannot be learned quickly, and time, which is all we have, must be paid heavily for their acquiring. They are the very simplest things, and because it takes a man’s life to know them the little new that each man gets from life is very costly and the only heritage he has to leave.” - Ernest Hemingway (More…)
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Daniel Raban's Note Repository Notes (Vietnamese)
Daniel Raban’s Note Repository # [UCLA] Math 206A: Combinatorial Discrete Geometry (Igor Pak, F18): PDF, PDF (Vi) [UCLA] Math 206B: Algebraic Combinatorics (Igor Pak, W19): [PDF], PDF (Vi) [UCLA] Math 210A: Algebra (Romyar Sharifi, F18): PDF, PDF (Vi) [UCLA] Math 210B: Algebra (Romyar Sharifi, W19): PDF, PDF (Vi) [UCLA] Math 210C: Algebra (Romyar Sharifi, Sp19): [PDF], PDF (Vi) [UCLA] Math 245B: Real Analysis (Tim Austin, W19): PDF, PDF (Vi) [UCLA] Math 245C: Real Analysis (Wilfrid Gangbo, Sp19): PDF, PDF (Vi) [UCLA] Math 246A: Complex Analysis (John Garnett, F18): [PDF], PDF (Vi)
List of Ebooks for Data Science
The Law - The mathematical foundations # Statistical Inference - Casella & Berger Foundations of Applied Mathematics History - Foundational works that provide additional context for more advanced concepts # Convex Optimization - Boyd & Vandenberghe Probability Theory: The Logic of Science - Jaynes Clean Code - Martin Poetry - Prose type works # The Art of Data Analysis Why Predictions Fail Weapons of Math Destruction Major Prophets - Seminal works on major topics # Applied Regression Analysis - Draper & Smith
List of Github Repository for Data Science
The Data Engineering Cookbook, Github A curated list of data engineering tools for software developers, Github Data Engineering Zoomcamp, Github Python Data Science Handbook: full text in Jupyter Notebooks, Github Data Science for Beginners - A Curriculum, Github Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines. Github Papers & tech blogs by companies sharing their work on data science & machine learning in production. Github An awesome Data Science repository to learn and apply for real world problems. Github List of Data Science Cheatsheets to rule the world, Github Data science interview questions and answers, Github A curated list of applied machine learning and data science notebooks and libraries across different industries, Github A curated list of data science blogs, Github
Optimization Research Papers in JMLR Volume 23
Optimization Research Papers in JMLR Volume 23 (2022) # This document lists papers from JMLR Volume 23 (2022) that focus on optimization research, categorized by their primary themes. Each paper is numbered starting from 1 within its subsection, with a brief description of its key contributions to optimization theory, algorithms, or applications. Convex Optimization # Papers addressing convex optimization problems, including sparse PCA, L1-regularized SVMs, and metric-constrained problems. Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality Authors: Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet Description: Develops convex optimization techniques for large-scale sparse principal component analysis with certifiable near-optimal solutions.
Optimization Research Papers in JMLR Volume 22
Optimization Research Papers in JMLR Volume 22 (2021) # This document lists papers from JMLR Volume 22 (2021) that focus on optimization research, categorized by their primary themes. Each paper is numbered starting from 1 within its subsection, with a brief description of its key contributions to optimization theory, algorithms, or applications. Convex Optimization # Papers addressing convex optimization problems, including clustering, Wasserstein barycenters, sparse optimization, and bandits. Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm Authors: Defeng Sun, Kim-Chuan Toh, Yancheng Yuan Description: Proposes a convex clustering model with theoretical guarantees and an efficient algorithm.