Become an LLM Expert

⏱ 2 – 3 months

• Linear Algebra — vectors, matrices, eigendecomposition, SVD
• Probability & Statistics — Bayes theorem, distributions, MLE, MAP
• Calculus & Optimization — gradients, chain rule, SGD, Adam
• Python Mastery — NumPy, pandas, matplotlib, Jupyter
• ML Fundamentals — regression, classification, bias-variance, cross-validation
• Deep Learning Basics — perceptrons, backprop, CNNs, RNNs, loss functions, regularization

⏱ 2 – 3 months

• Text Preprocessing — tokenization (BPE, WordPiece, SentencePiece), stemming, lemmatization
• Word Embeddings — Word2Vec, GloVe, FastText
• Sequence Models — RNNs, LSTMs, GRUs, seq2seq, attention mechanism
• Transformers Deep Dive — self-attention, multi-head attention, positional encoding, layer norm
• Pre-trained Models — BERT, GPT-2, T5, encoder vs decoder vs encoder-decoder
• Hugging Face Ecosystem — Transformers lib, Tokenizers, Datasets, Model Hub

⏱ 3 – 4 months

• Scaling Laws — Chinchilla, compute-optimal training, data vs parameters trade-off
• Modern Architectures — GPT-3/4, LLaMA, Mistral, Mixtral (MoE), Mamba (SSMs)
• Training Pipeline — data collection, cleaning, deduplication, curriculum learning
• Distributed Training — data parallelism, model parallelism, pipeline parallelism, ZeRO, FSDP
• Efficient Attention — FlashAttention, Multi-Query Attention, Grouped-Query Attention, RoPE, ALiBi
• Tokenizer Design — BPE variants, vocabulary size trade-offs, multilingual tokenization
• Infrastructure — GPU clusters, mixed-precision (FP16/BF16), checkpointing, Megatron-LM

⏱ 2 – 3 months

• Supervised Fine-Tuning (SFT) — instruction tuning, chat formatting, data quality
• RLHF — reward modeling, PPO, best practices
• DPO & Alternatives — Direct Preference Optimization, ORPO, KTO, SimPO
• Parameter-Efficient Methods — LoRA, QLoRA, prefix tuning, adapters, DoRA
• Data Engineering — synthetic data generation, data mixing, quality filtering
• Evaluation — perplexity, BLEU, ROUGE, MMLU, HumanEval, MT-Bench, Arena Elo
• Quantization — GPTQ, AWQ, GGUF, bitsandbytes, quantization-aware training

⏱ 3 – 4 months

• Prompt Engineering — chain-of-thought, few-shot, self-consistency, tree-of-thought
• RAG Systems — chunking, vector databases, embedding models, re-ranking, hybrid search
• AI Agents — function calling, tool use, ReAct, planning, multi-agent systems
• Inference Optimization — KV-cache, speculative decoding, vLLM, TensorRT-LLM, continuous batching
• Serving & Deployment — API design, load balancing, monitoring, cost optimization
• Frameworks — LangChain, LlamaIndex, Semantic Kernel, DSPy, CrewAI
• Guardrails & Safety — content filtering, jailbreak prevention, output validation

⏱ Ongoing

• Multimodal Models — vision-language (GPT-4V, LLaVA), audio, video understanding
• Reasoning & Planning — o1-style reasoning, chain-of-thought distillation, search + LLMs
• Long Context — context extension, ring attention, landmark attention, infinite context
• Efficient Models — distillation, pruning, NAS, small language models (SLMs)
• AI Safety & Alignment — Constitutional AI, RLAIF, interpretability, mechanistic interpretability
• Synthetic Data & Self-Improvement — self-play, self-instruct, evol-instruct
• World Models & Embodied AI — grounding, robotics integration, simulation
• Contributing to Open Source — publish papers, contribute to frameworks, build community