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Complexity Research Team (CRT)

"Chaos is the beginning, simplicity is the end.", M.C. Escher

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Neural Networks – Deep Learning

Posted on 12/11/202008/03/2022 by karaka
Posted in Deep Learning, Educational Tutorials, Neural Networks

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Complexity Theory Courses
Machine learning analysis of chaos and vice versa

Recent Posts

  • Spatial constrains and information content of sub-genomic regions of the human genome 11/09/2022
  • Special Issue “Coexistence of Complexity Metrics and Machine-Learning Approaches for Understanding Complex Biological Phenomena” 15/12/2020
  • Professor Robert Sapolsky: “Chaos and Reductionism” 25/11/2020

RSS Machine Learning

  • Universal AI is “a pathway to AI fluency that’s accessible and approachable to anyone, anywhere” 12/05/2026
    New AI education program from MIT Open Learning debuts with AI-powered personalization and a free introductory course for learners everywhere.
    Carolyn Tiernan | MIT Open Learning
  • Games people — and machines — play: Untangling strategic reasoning to advance AI 05/05/2026
    Assistant Professor Gabriele Farina mines the foundations of decision-making in complex multi-agent scenarios.
    Michaela Jarvis | MIT Laboratory for Information and Decision Systems
  • Beacon Biosignals is mapping the brain during sleep 01/05/2026
    Founded by Jake Donoghue PhD ’19 and former MIT researcher Jarrett Revels, the company is creating an AI-driven platform to help diagnose and treat disease.
    Zach Winn | MIT News

RSS Biology and Genetics

  • MIT BrainTrust supports neighbors living with brain injuries 06/05/2026
    Nearly 100 MIT students participate in a buddy program that assists Boston-area residents.
    Sarah Foote | Division of Student Life
  • Biologist Joey Davis explores how cells build complex structures 05/05/2026
    His studies have shed light on the assembly instructions that govern ribosomes, the critical protein-building machines of the cell.
    Anne Trafton | MIT News
  • Rett syndrome study highlights potential for personalized treatments 04/05/2026
    Using advanced human cell cultures, MIT researchers tracked how two different mutations alter neural circuit development, and how each could be addressed with distinct potential therapeutics.
    David Orenstein | The Picower Institute for Learning and Memory

Machine Learning Mastery

  • Choosing the Right Agentic Design Pattern: A Decision-Tree Approach
  • LLM Observability Tools for Reliable AI Applications
  • Implementing Prompt Compression to Reduce Agentic Loop Costs
  • Implementing Permission-Gated Tool Calling in Python Agents
  • The Roadmap to Mastering Tool Calling in AI Agents

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