Programming
Software development: programming languages, coding, libraries and frameworks, software engineering and architecture, source control, developer tools, databases, and infrastructure.
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Demaine, Gasarch & Hajiaghayi. Computers and Intractability: A Guide to Algorithmic Lower Bounds hardness.mit.edu
A sequel to Garey and Johnson's Computers and Intractability: A Guide to NP-Completeness. New topics include Parameterized Complexity, Lower bounds on approximation, Other hardness assumptions (ETH, 3SUM-conjecture, APSP-conjecture, UGC, Others), Online Algorithms, Streaming…
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Knuth. The Art of Computer Programming en.wikipedia.org
A legendary series by Donald Knuth on design and analysis of algorithms.
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Theory Announcements dmatheorynet.blogspot.com
DMANET spreads information on conferences, workshops, seminars etc. relating to discrete mathematics and algorithms.
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Williamson & Shmoys. The Design of Approximation Algorithms designofapproxalgs.com
It includes greedy, local search algorithms, dynamic programming, linear and semidefinite programming, and randomization.
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Salamon's List cstheory.stackexchange.com
Selected Conferences.
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Aspnes. Notes on Randomized Algorithms cs.yale.edu
Supplemental notes to the standard books by Mitzenmacher & Upfals, and Motwani & Raghavan.
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First cs.ubc.ca
and Second Course in Randomized Algorithms. Columbia. - Respectively, undergrad and grad courses for probabilistic methods in algorithms.
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Livni. COS 511 Theoretical Machine Learning. Princeton cs.princeton.edu
Formally define and study various models that have been proposed for learning. The course will present and contrast the statistical, computational and online models for learning. We will present and rigorously analyze some of the most successful algorithms in machine learning…
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Arora. Overcoming Intractability in Machine Learning cs.princeton.edu
A seminar course that will focus on the following phenomenon: many problems in machine learning are formally intractable (e.g., NP-hard). Nevertheless they are solved in practice by heuristics. Can we design algorithms with provable guarantees (running time, solution quality)?
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Arora. Advanced Algorithm Design cs.princeton.edu
Notably uses ideas such as randomness, approximation, high dimensional geometry. Faces uncertainty, approaches to handle big data, handling intractability, heuristic approaches, ..etc.
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Robert Robere. Proof Complexity: Algorithms and Lower Bounds cs.mcgill.ca
An introduction to modern proof complexity, emphasizing its connections with computational complexity and algorithms in optimization.
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Dinitz. Approximation Algorithms. Johns Hopkins cs.jhu.edu
It includes greedy, local search, dynamic programming, randomized rounding, tree embeddings, and semidefinite programming.
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Cornell. Functional Programming in Ocaml cs.cornell.edu
A modern course on data structures and functional programming using OCaml.
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Gupta & Ravi. Approximation Algorithms. CMU cs.cmu.edu
It includes convex programming-based, randomness, and metric methods.
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Introduction to Discrete Mathematics for Computer Science. UC San-Diego coursera.org
Learn the language of Computer Science. Learn the math that defines computer science, and practice applying it through mathematical proofs and Python code.
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Cambridge Foundations of CS cl.cam.ac.uk
It teaches programming and presents some fundamental principles of computer science, especially algorithm design.
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Chekuri. Topics in Algorithms: Algorithmic Game Theory chekuri.cs.illinois.edu
A broad graduate-level introduction to: auctions, existence and computation of equilibria in games and markets, algorithmic mechanism design, price of anarchy and price of stability, games relevant to networks and e-commerce. The emphasis will be on conceptual ideas and…
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Shalev-Shwartz. Understanding Machine Learning: From Theory to Algorithms cambridge.org
It provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.
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Tu Delft. Delftse Foundations of Computation books.open.tudelft.nl
A textbook for a one quarter introductory course in theoretical computer science including logic, proof techniques, and set theory. It assumes no prerequisite other than basic programming.
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Algorithms with Predictions algorithms-with-predictions.github.io
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Color Blind Tensors youtube.com
Manipulate image tensors to perceive color blindness, live coding.
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Laurence Moroney twitter.com
Lead of AI Developer Relations at Google.
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Jason Mayes twitter.com
Developer advocate for TensorFlow.js.
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Deep Learning with JavaScript manning.com
Learn to use TensorFlow.js to build deep learning models that run directly in the browser, master all the basics of deep learning and explore advanced concepts, like retraining existing models for transfer learning and image generation by Shanqing Cai, Stanley Bileschi, Eric D…
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API Reference js.tensorflow.org
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Danfo.js danfo.jsdata.org
High-performance, intuitive, and easy-to-use data structures for manipulating and processing structured data. (Inspired by Pandas)
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Google Codelabs codelabs.developers.google.com
Google Developers Codelabs provide a guided, tutorial, hands-on coding experience.
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Official TensorFlow.js blog blog.tensorflow.org
Covers building models in JavaScript and using them in the browser or in Node.js.
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TensorFlow.js: Machine Learning for the Web and Beyond arxiv.org
This paper describes the design, API, and implementation of TensorFlow.js, and highlights some of the impactful use cases.
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Practical Machine Learning in JavaScript apress.com
TensorFlow.js for Web Developers by Charlie Gerard.
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AiJS aijs.rocks
A curated collection of inspirational AI-powered JavaScript apps.
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Vanna.ai vanna.ai
An open-source Python RAG framework for SQL generation and related functionality. #opensource
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OpenAI Playground platform.openai.com
Explore resources, tutorials, API docs, and dynamic examples.
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OpenAI API openai.com
Announcement of the OpenAI API for text-to-text general-purpose AI models based on GPT-3. OpenAI blog, June 11, 2020.
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OpenAI API openai.com
OpenAI's API provides access to GPT models for natural language, coding, image generation, audio, and agent development.
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Open Notebook open-notebook.ai
An open source implementation of NotebookLM with more flexibility and features. #opensource
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Mubert mubert.com
A royalty-free music ecosystem for content creators, brands and developers.
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LLM llm.datasette.io
A CLI utility and Python library for interacting with Large Language Models, remote and local. #opensource
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Llama llama.com
Meta's open source large language model. #opensource
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Learn Prompting learnprompting.org
A free, open source course on communicating with artificial intelligence.
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Kimi kimi.com
An AI assistant by Moonshot AI with chat, deep research, coding, and multi-agent capabilities.
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Kilo kilo.ai
Open-source AI coding assistant for VS Code, JetBrains, and the CLI. #opensource
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HyperFrames hyperframes.heygen.com
A framework for AI agents to render videos by writing HTML, CSS, and JavaScript. #opensource
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Bloom huggingface.co
BLOOM by Hugging Face is a model similar to GPT-3 that has been trained on 46 different languages and 13 programming languages. #opensource
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Groq groq.com
A cloud inference API for running open-source LLMs, powered by custom LPU hardware.
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Grok grok.x.ai
An LLM by xAI with open source and open weights. #opensource
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Wren AI getwren.ai
An open-source text-to-SQL and generative BI agent with a semantic layer. #opensource
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fal fal.ai
A developer platform for accessing and deploying image, video, audio, and 3D generation models.
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DiffusionDB diffusiondb.com
A list of all public apps, developer tools, guides and plugins for Stable Diffusion. Airtable version.
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Devin devin.ai
An autonomous AI software engineer by Cognition Labs.
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