Top Ten Terms in Statistical Mechanics

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Course curriculum

1. Day 0: Welcome and Learning Objectives (VID)
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2. Day 0: Why Specific Claude File Uploads (TXT)
3. Day 0: How to Set Up Your Claude "AI Trail Guide" (TXT)
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4. Day 0: "Top Ten Terms" (DWNLD for You and Claude)
5. Day 0: "Top Ten Terms Trail Guide Orientation (DWNLD - for Claude)
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1. Week 1: "T3 Week 1 - Video Transcripts - Days 0 - 5" (DWNLD for Claude)
2. Week 1: "T3 Week 1 - Consolidated PDFs" (DWNLD for Claude)
3. Week 1: "T3 Day 3 Supp Vid JPGS" (DWNLD for Claude)
4. Week 1: "T3 Week 1 - Exercises" (DWNLD for Claude)
5. Week 1: Salakhutdinov & Hinton (2012); "Deep Learning"
1. Day 1: What You'll Learn in This Course (VID)
2. Day 1: Introduction and Overview (PDF)
3. Day 1: Resource - The Seven Key Equations Used in Generative AI (PDF of Single Slide)
4. Day 1: Resource - Salakhutdinov and Hinton (2012) (Citation & Link)
5. Day 1: "How to Learn Energy-Based Neural Networks" (YouTube Link; Extra Resource for Day 1 Exercise)
6. Day 1: Exercise: Read Intro of S&H (2012); Identify Key Terms (EXERCISE)
7. Day 1: Exercise 2: COPY DOWN and ANNOTATE the Material in the "Seven Equations" Slide
1. Day 2: Thermodynamics (Video)
2. Day 2: Thermodynamics (PDF)
3. Day 2: Exercise: Thermodynamic Reflections (EXERCISE)
1. Day 3: Free Energy (Video)
2. Day 3: Free Energy Overview (PDF)
3. Day 3: S&H 2012 Paragraph 1 (Decoded) (Video)
4. Day 3: S&H 2012 Paragraph 1 (Decoded) (PDF)
5. Day 3: Supplemental - Hinton Describing (Restricted) Boltzmann Machines (Links to Hinton's Slidedecks)
6. Day 3: Exercise - Free Energy Reflections (EXERCISE)
1. Day 4: Enthalpy (Video)
2. Day 4: Enthalpy (PDF)
3. Day 4: Enthalpy Reading Assignment - SMNNAI (book) Chapter 9: Hopfield and Boltzmann Machine
4. Day 4: Enthalpy - Supplemental Video (YouTube Link - "The Matrix: Framework for a New Class of Neural Networks")
5. Day 4: Enthalpy - Supplemental Reading (Blogpost Link - "When a Classifier ...")
6. Day 4: Exercise - Enthalpy Reflection (EXERCISE)
1. Day 5: Entropy (Video)
2. Day 5: Entropy Analogy (Light, Fun) (Supplemental Video)
3. Day 5: Entropy (PDF)
4. Day 5: Entropy: Tutorial (Blogpost Link - "Wrapping Our Heads around Entropy")
5. Day 5: Entropy: PDF of "Wrapping Our Heads around Entropy" Blogpost (Supplemental PDF)
6. Day 5: Entropy: Interesting/Useful Tutorial (Supplemental Blogpost Link - "What We Really Need to Know about Entropy")
7. Day 5: Supplemental Exercise - Map the Entropy Function (SUPPL EXERCISE)
8. Day 5: Exercise - Entropy Reflection (EXERCISE)
1. Day 6: Entropy: Supplemental Resource - Wolfram on Entropy (YouTube Link)
2. Day 6: Exercise - Integration (EXERCISE)
1. Day 7: Meditation on Free Energy, Enthalpy, and Entropy
2. Day 7: Meditation on Enthalpy and Perfection (Fun-Play Exercise!)
3. Day 7: Send Me an Email
4. Day 7: Exercise - Meditation (EXERCISE)
1. Week 2: "T3 Week 2 Video Transcripts - Days 8 - 14" (DWNLD - for Claude)
2. Week 2: "T3 Week 2 - Consolidated PDFS" (DWNLD for Claude)
3. Week 2: "T3 Week 2 Exercises" (DWNLD for Claude)
1. Day 8: Microstates (VID)
2. Day 8: Statistical Mechanics is a Metaphor, Not a Model (YouTube Link)
3. Day 8: Statistical Mechanics (PDF)
4. Day 8: Microstates (PDF)
5. Day 8: Microstates Reading Assignment - SMNNAI (book) Chapter 10:"Introduction to Statistical Mechanics: Microstates and the Partition Function"
6. Day 8: Exercise - Microstates (EXERCISE)
1. Day 9: Stat Mech Overview ( Connection to Generative AI) (VID)
2. Day 9: Entropy from a Statistical Mechanics Perspective (Vid)
3. Day 9: Entropy from a Statistical Mechanics Perspective (PDF)
4. Day 9: Entropy - Explained in Terms of Probabilities and Surprise (YouTube Link)
5. Day 9: Entropy - Jason Brownlee's Explanation
6. Day 9: Entropy (Two YouTube Links)
7. Day 9: Exercise - Entropy (Stat Mech Perspective) (EXERCISE)
1. Day 10: The Partition Function - Introduction (Video)
2. Day 10: The Partition Function - Full Tutorial (Video)
3. Day 10: The Partition Function (PDF)
4. Day 10: Exercise - Partition Function (EXERCISE)
1. Day 11: The Ising Model - Simple Experiments (Video)
2. Day 11: The Ising Equation (YouTube #Short Link - "The Ising Equation Lets You Understand All Energy-Based Neural Networks")
3. Day 11: Free Energy and the Ising Equation (PDF)
4. Day 11: Free Energy and the Ising Equation Reading Assignment - SMNNAI (book) Chapter 11: "Free Energy"
5. Day 11: Excel Spreadsheet (Download Link)
6. Day 11: Exercise - Free Energy (EXERCISE)
1. Day 12: Equilibrium (Video)
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2. Day 12: Equilibrium (PDF)
3. Day 12: Exercise - Equilibrium (EXERCISE)
1. Week 2: Day 13 - Integration (EXERCISE)
1. Day 14: Meditating on an Equation (Video)
2. Day 14: Sunday Meditation on Paperwork and Entropy (Exercise)
1. Day 15: The REVERSE Kullback-Leibler Divergence and Generative AI (Video)
2. Day 15: The K-L Divergence: Resources (Blogpost Links)
3. Day 15: The Reverse KL Divergence (PDF, Substantive Read)
1. Day 16: Bayesian Probability and Stat Mech: Confluence of Two Worlds (Video)
2. Day 16: Generative AI and Bayesian Probabilities (YouTube Link)
3. Day 16: The Most Important Blogpost You Can Read on Probabilities
4. Day 16: Bayesian Probabilities: Resources (Precis PDF and Wiki Links)
1. Day 17: Generative AI Moves in Three Directions (Video)
2. Day 17: Energy-Based NN Historical Intro (YouTube #short - "How to Identify the First Energy-Based Neural Network")
3. Day 17: "The Boltzmann Machine" (YouTube #short Link)
4. Day 17: Supplemental Resource (YouTube Link: "A New Neural Network Class: Creating the Framework" on Energy-Based Neural Networks)
5. Day 17: Reading Assignment (Re-Read): "The Hopfield Neural Network and the (Restricted) Boltzmann Machine" - Chapter 9 (from SMNNAI)
1. Day 17.5: Kingma and Welling on Variational Autoencoders (PDF Link)
2. Day 17.5: Auto-Encoders - Yet Another form of Generative AI
1. Day 18: Derivation of Variational Bayes Equations (A 70-Page Tutorial Paper - PDF)
2. Day 18: Variational Inference - My Personal Blooper Reel (Blogpost Link)
1. Day 19: Active Inference (YouTube Link: Friston's Use of "Psi")
2. Day 19: Active Inference and Renormalising Generative Models: Why They Are Important
1. Day 20: Generative AI and AGI (Artificial General Intelligence) (YouTube Link)
1. Day Final: Book "Statistical Mechanics, Neural Networks, and AI": Precis and Selected Chapters (LINKS)
2. Day 4: Enthalpy Exercise (Reflection and Play) (Supplemental)

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99 lessons
3.5 hours of video content

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Top Ten Terms in Statistical Mechanics

Course curriculum

Day 0: Course Overview and Getting Started

Week 1: Thermodynamics - Your Claude Upload Files

Day 1: Introduction

Day 2: Thermodynamics

Day 3: Free Energy

Day 4: Enthalpy

Day 5: Entropy

Day 6: Week 1 Summary & Review Assignment; Weekend Podcasts

Day 7: Week 1 Meditation

Week 2: Statistical Mechanics: Overview and Preparations

Day 8: Statistical Mechanics and Microstates

Day 9: Entropy (Statistical Mechanics Perspective)

Day 10: The Partition Function

Day 11: Free Energy and the Ising Equation

Day 12: Equilibrium

Day 13: Week 2 Summary and Review Assignment; Weekend Podcasts

Day 14: Week 2 Meditation

Day 15: The Kullback-Leibler Divergence

Day 16: Bayesian Probabilities

Day 17: Energy-Based Neural Networks

Day 17.5: Variational Auto-Encoders (Kingma and Welling)

Day 18: Variational Inference

Day 19: Active Inference

Day 20: Summary and Final Exercise

Day Final: References and Resources - Further Independent Study

About this course

Discover your potential, starting today