NLP
NLP (12): Frontiers and Practical Applications
Series finale: agents and tool use (Function Calling, ReAct), code generation (Code Llama, Codex), long-context attention (Longformer, Infini-attention), reasoning models (o1, R1), safety and alignment, evaluation, and …
NLP (11): Multimodal Large Language Models
A deep dive into multimodal LLMs: contrastive vision-language pre-training with CLIP, parameter-efficient bridging with BLIP-2's Q-Former, visual instruction tuning with LLaVA, robust speech recognition with Whisper, …
NLP (10): RAG and Knowledge Enhancement Systems
Build production-grade RAG systems from first principles: the retrieve-then-generate decomposition, vector indexes (FAISS / Milvus / Chroma / Weaviate / Pinecone), dense+sparse hybrid retrieval with RRF, cross-encoder …
NLP (9): Deep Dive into LLM Architecture
Inside modern LLMs: pre-norm + RMSNorm + SwiGLU + RoPE + GQA, KV cache mechanics, FlashAttention's IO-aware schedule, sparse Mixture-of-Experts, and INT8 / INT4 quantization.
NLP (8): Model Fine-tuning and PEFT
A deep dive into Parameter-Efficient Fine-Tuning. Why LoRA's low-rank update works, the math and memory accounting behind QLoRA, how Adapters and Prefix-Tuning differ, and how to choose between them in production.
NLP (7): Prompt Engineering and In-Context Learning
From prompt anatomy to chain-of-thought, self-consistency and ReAct: a working theory of in-context learning, the variance you have to fight, and the patterns that scale to real systems.
NLP Part 6: GPT and Generative Language Models
From GPT-1 to GPT-4: understand autoregressive language modeling, decoding strategies (greedy, beam search, top-k, top-p), in-context learning, and build a chatbot with HuggingFace.
NLP Part 5: BERT and Pretrained Models
How BERT made bidirectional pretraining the default in NLP. We unpack the architecture, the 80/10/10 masking rule, fine-tuning recipes, and the RoBERTa/ALBERT/ELECTRA family with HuggingFace code.
NLP Part 4: Attention Mechanism and Transformer
From the bottleneck of Seq2Seq to Attention Is All You Need. Build intuition for scaled dot-product attention, multi-head attention, positional encoding, masking, and assemble a complete Transformer in PyTorch.
NLP Part 3: RNN and Sequence Modeling
How RNNs, LSTMs, and GRUs process sequences with memory. We derive vanishing gradients from first principles, build a character-level text generator, and implement a Seq2Seq translator in PyTorch.
NLP Part 2: Word Embeddings and Language Models
Understand how Word2Vec, GloVe, and FastText turn words into vectors that capture meaning. Learn the math, train your own embeddings with Gensim, and connect embeddings to language models.
NLP Part 1: Introduction and Text Preprocessing
A first-principles introduction to NLP and text preprocessing. We trace the four eras of the field, build the cleaning to vectorization pipeline by hand, and unpack the math behind tokenization, TF-IDF, n-grams, and …
Position Encoding Brief: From Sinusoidal to RoPE and ALiBi
A practitioner's tour of Transformer position encoding: why attention needs it at all, how sinusoidal/learned/relative/RoPE/ALiBi schemes differ, and which one to pick when long-context extrapolation matters.
Multimodal LLMs and Downstream Tasks: A Practitioner's Guide
End-to-end map of multimodal LLMs: vision-language alignment, cross-modal fusion, the CLIP/BLIP/LLaVA families, downstream tasks (VQA, captioning, grounding, OCR), fine-tuning trade-offs, benchmarks, and what it takes to …