ByteByteAI – Learn by Doing. Become an AI Engineer
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ByteByteAI – Learn by Doing. Become an AI Engineer
Taught by Best-Selling Author Ali Aminian
About Ali Aminian
Ali Aminian is a best-selling author of multiple books on machine learning and generative AI. With over a decade of experience at leading tech companies, he has built AI systems that are intelligent, safe, and efficient. He also contributes to AI courses at Stanford University, combining technical expertise with a passion for teaching.
What You’ll Learn In Learn by Doing. Become an AI Engineer
Project 1: Build an LLM Playground
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, DeepSeek, Qwen, Gemma)
- 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
Chatbots’ Overall Design
Project 2: Build a Customer Support Chatbot using RAGs and Prompt Engineering
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
Project 3: Build an “Ask-the-Web” Agent similar to Perplexity with Tool calling
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)
Agent Evaluation
Project 4: Build “Deep Research” Capability with Web Search and Reasoning Models
Reasoning and Thinking LLMs
- Overview of reasoning models like OpenAI’s “o” family and DeepSeek-R1
Inference-time Techniques
- Inference-time scaling
- CoT prompting
- Parallel sampling
- Sequential sampling
- 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)
Local Deployment
Project 5: Build a Multi-modal Generation Agent
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
Project 6: Capstone Project
Ship a portfolio-ready AI project from idea to demo
- Choose: pick your own idea, or start from a curated list
- Build: implement using techniques from the course
- Iterate: get real-time feedback from the instructor as you build
- Optional Demo: present your project on final demo day
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