The basic "Learning AI" collection

[Chip (OP)]

This should get you up to speed, in this order:

1. The First AI - Samuel’s Checkers ML System
https://forum.drugs-and-users.org/index.php?topic=7338

Historical grounding.
Shows the origins of machine learning and self-improving systems.

2. What the basic components of AI are and how the data flows
https://forum.drugs-and-users.org/index.php?topic=7347

High-level architectural overview before deep diving.

3. How Neural Networks Work
https://forum.drugs-and-users.org/index.php?topic=7343

Core foundation.
Everything modern comes from this.

4. The AI Tokenisation Pipeline
https://forum.drugs-and-users.org/index.php?topic=7350

Now the reader understands WHY text must become vectors and embeddings.

5. Transformers
https://forum.drugs-and-users.org/index.php?topic=7344

The real breakthrough architecture behind modern LLMs.

6. A light intro to LLMs, chatbots, pretraining, and transformers
https://forum.drugs-and-users.org/index.php?topic=7342

Applies the transformer concept to actual LLM systems and chatbot behaviour.

7. RAG - Retrieval Augmented Generation
https://forum.drugs-and-users.org/index.php?topic=7348

Advanced modern extension layer.
Shows how models interface with external knowledge.


Those were the basics and once you roughly understand them then continue on with the following topics:


8. Embeddings and Vector Spaces
https://forum.drugs-and-users.org/index.php?topic=7351

Right now embeddings are probably buried inside tokenisation or neural networks, but embeddings are absolutely central to modern AI.

Topics:

• What embeddings actually are
• High-dimensional vector spaces
• Semantic proximity
• Why "cat" and "dog" cluster together
• Cosine similarity
• Latent space
• Why RAG works
• Why hallucinations happen

This becomes the bridge between:

Code: [Select]

Token IDs → Meaning Space

9. Attention Mechanisms and Self-Attention
https://forum.drugs-and-users.org/index.php?topic=7352

Transformers really deserve to be split and Attention is the actual revolutionary mechanism.

Topics:

• Query / Key / Value vectors
• Attention weighting
• Context windows
• Token relationships
• Parallel processing vs recurrence
• Why transformers replaced RNNs/LSTMs

Without attention, transformers look like magic.

10. Training vs Inference
https://forum.drugs-and-users.org/index.php?topic=7353

This is one of the most misunderstood things in AI discussions.

Most people think ChatGPT is "learning while talking."

It usually is not.

Topics:

• Pretraining
• Gradient descent
• Backpropagation
• Weights
• Inference-only operation
• Fine tuning
• RLHF
• Why models are static snapshots

This clears up enormous confusion.

11. Context Windows and Memory
https://forum.drugs-and-users.org/index.php?topic=7354

Critical for chatbot understanding.

Topics:

• What context windows are
• Token limits
• Sliding attention windows
• Conversation truncation
• Why models "forget"
• Persistent memory systems
• RAG vs memory

This directly explains chatbot behaviour.

12. Hallucinations and Failure Modes
https://forum.drugs-and-users.org/index.php?topic=7355

Very important as AIs won't say "I don't know".

Topics:

• Probabilistic generation
• Why confidence ≠ correctness
• Distribution gaps
• Mode collapse
• Confabulation
• Context poisoning
• Prompt injection

Most people fundamentally misunderstand hallucinations.

13. Multi-Modal AI
https://forum.drugs-and-users.org/index.php?topic=7356

Modern systems are no longer just text.

Topics:

• Vision transformers
• Image tokenisation
• Audio embeddings
• Cross-modal embeddings
• Unified latent spaces
• Image generation diffusion models

This connects LLMs to image/video/audio systems.

14. Agents and Tool Use
https://forum.drugs-and-users.org/index.php?topic=7357

Modern frontier AI architecture.

Topics:

• Tool calling
• External APIs
• Planning loops
• Chain-of-thought orchestration
• Autonomous agents
• Memory stores
• Execution environments

This is where systems are heading now.

15. Scaling Laws
https://forum.drugs-and-users.org/index.php?topic=7358

Very important historically.

Topics:

• Why bigger models suddenly worked
• Emergent behaviour
• Parameter scaling
• Data scaling
• Compute scaling
• Why GPT-3 changed everything

16. Quantisation and Model Compression
https://forum.drugs-and-users.org/index.php?topic=7360

The practical consequence of Scaling Laws.

Topics:

• What model weights actually are at the bit level
• FP32 vs FP16 vs INT8 vs INT4
• How precision reduction affects output quality
• GGUF and GGML formats
• llama.cpp and local inference
• Pruning and knowledge distillation
• Why a 7B quantised model can run on a laptop

Scaling Laws explains why models got enormous.
This explains how ordinary hardware runs them anyway.
Directly relevant to anyone self-hosting or running local models.

This explains why AI progress looked sudden.

17. Diffusion Models and What They Are For
https://forum.drugs-and-users.org/index.php?topic=7359

When discussing image generation.

Topics:

• Noise schedules
• Denoising
• Latent diffusion
• Classifier guidance
• Why Stable Diffusion works

Completely different architecture family from transformers.

That’s plenty for now !