“When you write a short story,… you had better know the ending first” Isaac Asimov

AI / AIOPS

Click @ for links

AI in companies 3109 Web3 dev
@AScherpenberg
  • @ ML/AI/Data-MAD Landscape via Matt Turck (2023 version below – still the latest as of 5/15/23)

OVERVIEWS

  • @ Halon AI Strategy Landscape
  • @ McKinsey: The State of AI (2023)
  • @ Deloitte/Snowflake: AI Success in 2024
  • @ State of AI Report 2023: Benaich & Hogarth
  • @ The MAD Landscape via Matt Turck at FirstMark (2023)

TRAINING

  • @ BIG Dev Resources
  • @ AI/ML Beginners Guide
  • @ O’Reilly Online Training
  • @ Analytics Vidhya Courses
  • @ Machine Learning Guide
  • @ R tutorial via DataCamp
  • @ Coursera: Machine Learning (Ng)
  • @ Coursera/Stanford:  ML Syllabus
  • @ Coursera: Neural Networks for Machine Learning (Hinton)
  • @ DZone: 35 Free Online Books on Machine Learning
  • @ Google: Machine Learning Glossary
  • @ Hadoop training via MapR
  • @ Hadoop with Python via Glennklockwood
  • @ Practical Machine Learning with Python(sentdex)
  • @ Oxford Deep NLP (Blunsom et al. 2017)
  • @ Stanford CS231n: Convolutional Neural Networks for Visual Recognition (2016)
  • @ Stanford CS224n: Natural Language Processing with Deep Learning (2017)
  • @ Statistics How To: The Practical Statistics Handbook
  • @ SQL tutorial via CodeAcademy & another via W3
  • @ SQL Data Warehousingvia Linda.com
  • @ Udacity: Machine Learning (Georgia Tech)
  • @ Udacity: Intro to Machine Learning (Thrun)
  • @ Udacity: Intro to TensorFlow for Deep Learning (TensorFlow)
  • @ AI/ML in Practice: four sections: Machine Learning, NLP, Python, and Maths (Robbie Allen 2018 Edition)

DATA SCIENCE CENTRAL

  • @ Free Books
  • @ Cheat Sheets
  • @ Enterprise AI – An Applications Perspective
  • @ Online Encyclopedia of Statistical Science (Free)

NEWSLETTERS

  • @ AI Weekly
  • @ The Exponential View
  • @ Data Science Weekly Newsletter
  • @ KDnuggets Newsletter
  • @ O’Reilly Artificial Intelligence Newsletter

PODCASTS

  • @ AI Podcast via a16z
  • @ AI Podcast by Lex Fridman
  • @ AI/ML Podcasts via KdNuggets
  • @ This Week in Machine Learning and AI
  • @ The AI Podcast by Nvidia
  • @ Data Skeptic Podcast
  • @ Linear Digressions
  • @ Learning Machines 101

CIO.com

  • @ Analytics Section
  • @ Why data analytics initiatives still fail
  • @ 4 tips for quick automation wins
  • @ 6 best practices for business data visualization
  • @ 9 emerging job roles for the future of AI
  • @ Where enterprise IT can really apply AI
  • @ 5 hurdles to AI value — and how to overcome them

TOOLS & OTHER MEDIA

  • @ Agile Manifesto via the authors @AM.org
  • @ Big Data Manifesto & The Big Data Institute
  • @ Snowflake “Gen AI and LLMs for Dummies” (2024)
  • @ Cheat Sheet of Machine Learning and Python (and Math) Cheat Sheets
  • @ CISO: Garbage In, Garbage Out is NOT Why Machine Learning Fails
  • @ DataRobot: Introduction to AI Storytelling
  • @ Data Science for Security Professionals by Charles Givre for O’Reily Media
  • @ DataStax Webcasts via DataStax
  • @ Data Driven Webcasts via Firstmark
  • @ DataBricks: Productionizing Machine Learning: From Deployment to Drift Detection
  • @ Rabbit Hole List II :  DL/NLP, ML, Python resources (Allen 2018)
  • @ Rabbit Hole List I: researchers, academic work, social links, and more (Allen 2017)
  • @ Data Science Weekly: Full List of Data Science Resources
  • @ Qubole: AI and Data Analytics Resources
  • @ AnalyticBridge: Data Science e-books, webinars, and resources

COMMENTARY

  • @ 60 Minutes: AI Stories (2019-2023)
  • @ FRONTLINE: In the Age of AI full film (2019)
  • @ YT Original: How Far is Too Far? | The Age of A.I. (2019)
  • @ DW: Artificial intelligence & algorithms: pros & cons (2019)
  • @ TechCrunch: Beyond AI with Sam Altman and Greg Brockman (2019)
  • @ Bloomberg: Y-Combinator’s Sam Altman Says AI Can Reset Global Equality (2018)

B.I.G.

CHAMPIONS MADE HERE