AI
Artificial intelligence: machine learning, neural networks, large language models, deep learning, AI research, and AI tools.
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Advanced Analytics with Spark shop.oreilly.com
Useful collection of Spark processing patterns. Accompanying GitHub repository: sryza/aas.
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PredictionIO prediction.io
Machine Learning server for developers and data scientists to build and deploy predictive applications in a fraction of the time.
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GraphLab Create dato.com
A machine learning platform in Python with a broad collection of ML toolkits, data engineering, and deployment tools.
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MLflow mlflow.org
Machine learning orchestration platform.
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ModelDB mitdbg.github.io
A system to manage machine learning models for spark.ml and scikit-learn .
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Datapallas datapallas.com
BI and data platform with AI exploration, dashboards, and pixel-perfect report generation; formerly ReportBurster.
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KeystoneML keystone-ml.org
Type safe machine learning pipelines with RDDs.
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Comet comet.com
Comet provides an end-to-end model evaluation platform for AI developers, with best in class LLM evaluations, experiment tracking, and production monitoring.
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Concurrent Pattern cascading.org
machine learning library for Cascading.
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2013 amplab.cs.berkeley.edu
AMPLab - Presto: Distributed Machine Learning and Graph Processing with Sparse Matrices.
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2013 amplab.cs.berkeley.edu
AMPLab - MLbase: A Distributed Machine-learning System.
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Data Science and Engineering with Apache Spark (edX XSeries) edx.org
Series of five courses (Introduction to Apache Spark, Distributed Machine Learning with Apache Spark, Big Data Analysis with Apache Spark, Advanced Apache Spark for Data Science and Data Engineering, Advanced Distributed Machine Learning with Apache Spark) covering different…
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Anthony Tellez anthonygtellez.github.io
Security and Machine Learning items.
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Zingg zingg.ai
Open source Master Data Management platform using machine learning for entity resolution at scale. Native to Databricks, Microsoft Fabric, Snowflake, AWS, and GCP. Golden records are maintained through a persistent Zingg ID across all systems and sources.
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Xquik xquik.com
Real-time X (Twitter) data extraction platform with REST API (76 endpoints), 20 bulk extraction tools, account monitoring, HMAC-signed webhooks, and MCP server for AI agent integration.
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Spark MLlib spark.apache.org
Spark's scalable machine learning library consisting of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as underlying optimization primitives.
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Rivestack rivestack.io
Managed PostgreSQL with pgvector for AI workloads. HNSW indexing, sub-4ms latency, and built-in SQL editor with automatic embedding generation.
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Practical AI practicalai.fm
Making AI practical, productive, and accessible to everyone.
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Learn AI Data Engineering in a Month of Lunches manning.com
A fast, friendly guide to integrating large language models into your data workflows.
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LunaPad lunapad.dev
Open-source analytics notebook for reusable SQL workflows, interactive reports, and AI-assisted data exploration.
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Latent Space latent.space
Technical deep dives on AI engineering, from model training to deployment.
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Bonnard bonnard.dev
Governed, multi-tenant MCP access to your customers' data. Turn your warehouse, dbt, or semantic layer into a secure, per-customer MCP for AI agents.
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AI for Database aifordatabase.com
Agentic AI platform to connect any database (PostgreSQL, MySQL, MongoDB, etc.) and query in plain English; includes self-refreshing intelligent dashboards and action workflows triggered by data changes.
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Gregory Valiant. The Modern Algorithmic Toolbox. Stanford web.stanford.edu
It covers hashing, dimension reduction, linear and convex programming, gradient descent and regression, sampling and estimation, compressive sensing, linear-algebraic techniques (principal components analysis, singular value decomposition, spectral techniques), and an intro to…
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Mary Wootters. Randomized Algorithms and Probabilistic Analysis. Stanford web.stanford.edu
Key tools of probabilistic analysis, and application of these tools to understand the behaviors of random processes and algorithms. Emphasis is on theoretical foundations, though applications will be discussed in machine learning and data analysis, networking, and systems…
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Simons' Institute simons.berkeley.edu
Programs, Events, and workshops, that aim toward maximizing impact and engagement across the theoretical computer science community.
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Moitra. Theoretical Foundations for Deep Learning. MIT people.csail.mit.edu
It explores theoretical foundations for deep learning, emphasizing the following themes: (1) Approximation: What sorts of functions can be represented by deep networks, and does depth provably increase the expressive power? (2) Optimization: Essentially all optimization…
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Telgarsky. Deep Learning Theory. Illinois mjt.cs.illinois.edu
Focuses on simplified proofs over what appears in the literature, and classical perspective of achieving a low test error for binary classification with IID data via standard (typically ReLU) feedforward networks.
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Vazirani & Kearns. An Introduction to Computational Learning Theory mitpress.mit.edu
Emphasizing issues of computational efficiency, It introduces a number of central topics in computational learning theory.
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Vaughan. CS260: Machine Learning Theory jennwv.com
A broad overview of the theoretical foundations underlying common machine learning algorithms.
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Blum. An Introduction to the Theory of Machine Learning. TTIC home.ttic.edu
The basic theory underlying machine learning and the process of generalizing from data.
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Xu. Topics in Learning and Game Theory haifeng-xu.com
A graduate level course covering topics at the interface between machine learning and game theory.
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Fang. Advanced Topics in Machine Learning and Game Theory feifang.info
A graduate-level course covering the topics at the intersection of machine learning and game theory.
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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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Agrawal & Jaiswal. When Machine Learning Meets AI and Game Theory cs229.stanford.edu
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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. The Computational Universe cs.princeton.edu
Takes us on a broad sweep of scientific knowledge and related technologies: propositional logic of the ancient Greeks (microprocessors); quantum mechanics (silicon chips); network and system phenomena (internet and search engines); computational intractability (secure…
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Arora. Thinking Like a Theorist. Princeton cs.princeton.edu
It covers a large number of the math/CS topics that you need to know for reading and doing research in Computer Science Theory.
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Blum. Intro Machine Learning Theory cs.cmu.edu
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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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Official TensorFlow.js video series youtube.com
The YouTube content focuses on TensorFlow ecosystem updates.
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Aaron Ma x.com
World's youngest TensorFlow contributor!
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Nikhil Thorat twitter.com
Co-lead of TensorFlow.js.
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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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Daniel Smilkov twitter.com
Co-creator of TensorFlow.js.
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Sandeep Gupta twitter.com
Product Manager for TensorFlow.
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Gant Laborde twitter.com
Web & Machine Learning GDE.
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