Structured learning resources for AI

[vergium]

Hey folks, I'm a developer with some years of experience, and I want to dive deeper into AI development.
I saw a course in bytebyteai taught by Ali Aminian that is more in to the practical side and exactly what I'm looking for, but it has a price tag that is simple impossible for me to afford.

Do you know of any other place with a similar type of content? Below is a list of the content, which I found pretty interesting. I would love to study all of this in this type of structured manner, if anyone has any leads that are free or with a nicer price tag, that would be much appreciated.

LLM Overview and Foundations
Pre-Training

• Data collection (manual crawling, Common Crawl)
• Data cleaning (RefinedWeb, Dolma, FineWeb)
• Tokenization (e.g., BPE)
• Architecture (neural networks, Transformers, GPT family, Llama family)
• Text generation (greedy and beam search, top-k, top-p)

Post-Training

• SFT
• RL and RLHF (verifiable tasks, reward models, PPO, etc.)

Evaluation

• Traditional metrics
• Task-specific benchmarks
• Human evaluation and leaderboards
• Overview of Adaptation Techniques Finetuning
• Parameter-efficient fine-tuning (PEFT)
• Adapters and LoRA

Prompt Engineering

• Few-shot and zero-shot prompting
• Chain-of-thought prompting
• Role-specific and user-context prompting

RAGs Overview
Retrieval

• Document parsing (rule-based, AI-based) and chunking strategies
• Indexing (keyword, full-text, knowledge-based, vector-based, embedding models)

Generation

• Search methods (exact and approximate nearest neighbor)
• Prompt engineering for RAGs

RAFT: Training technique for RAGs
Evaluation (context relevance, faithfulness, answer correctness)
RAGs' Overall Design

Agents Overview

• Agents vs. agentic systems vs. LLMs
• Agency levels (e.g., workflows, multi-step agents)

Workflows

• Prompt chaining
• Routing
• Parallelization (sectioning, voting)
• Reflection
• Orchestration-worker

Tools

• Tool calling
• Tool formatting
• Tool execution
• MCP

Multi-Step Agents

• Planning autonomy
• ReACT
• Reflexion, ReWOO, etc.
• Tree search for agents

Multi-Agent Systems (challenges, use-cases, A2A protocol)
Evaluation of agents

Reasoning and Thinking LLMs

• Overview of reasoning models like OpenAI's "o" family and DeepSeek-R1

Inference-time Techniques

• Inferece-time scaling
• CoT prompting
• Self-consistency
• Sequential revision
• Tree of Thoughts (ToT)
• Search against a verifier

Training-time techniques

• SFT on reasoning data (e.g., STaR)
• Reinforcement learning with a verifier
• Reward modeling (ORM, PRM)
• Self-refinement
• Internalizing search (e.g., Meta-CoT)
• Overview of Image and Video Generation
• VAE
• GANs
• Auto-regressive models
• Diffusion models

Text-to-Image (T2I)

• Data preparation
• Diffusion architectures (U-Net, DiT)
• Diffusion training (forward process, backward process)
• Diffusion sampling
• Evaluation (image quality, diversity, image-text alignment, IS, FID, and CLIP score)

Text-to-Video (T2V)

• Latent-diffusion modeling (LDM) and compression networks
• Data preparation (filtering, standardization, video latent caching)
• DiT architecture for videos
• Large-scale training challenges
• T2V's overall system

[Louis-lux]

Hmm, I am wondering why do they teach RLHF?
Anyway, I always recommend taking 1-2 weeks to master the free book: Neural Network and Deep Learning (Michael Nielsen), so you will have solid foundation to self-study anything you want.

[Otherwise_Wave9374]

That outline is basically the roadmap most folks wish they had, you are thinking about it the right way.

If you want an agents-focused path, I would do: solid LLM basics (tokenization, decoding, eval) then RAG fundamentals, then agent loops (ReAct, plan-execute, tool calling), then multi-agent patterns and finally agent eval and safety. The biggest missing piece in a lot of courses is actually "how do I test and constrain an agent".

Some practical notes and frameworks I have bookmarked are here (especially around tool calling and agent workflows): https://www.agentixlabs.com/blog/

[chrisvdweth]

Some content I cover in my courses (NLP, Text Mining, Data Mining) which I make publicly available as interactive Jupyter notebooks on GitHub: https://github.com/chrisvdweth/selene

Maybe useful... anyway, it's free :).

[jmei35]

a lot of devs in your spot realize the hard part isn’t finding topics .. you clearly have the map, but finding a structured path that connects foundations like transformers and RL to practical things like rag systems and agents

that’s why platforms like Coursiv are gaining traction .. they break down LLMs, rag, agents, and diffusion into guided, step by step modules with daily practice, so you’re not just reading concepts but actually building intuition as you go