The Stable Diffusion setup from L11 listens only to the prompt, so composition and pose are left to luck. L12 adds a steering wheel. ControlNet copies a block of SD and wires the copy back in through zero convolutions, so extra conditions such as edge maps, poses, and depth maps can steer generation. The standard example is Canny edges. The second half covers Fooocus, an SD interface that aims to be 'as simple as Midjourney': Presets, Styles, and the five Input Image features, where Image Prompt is ControlNet with a friendly wrapper. Week 12 homework: pick a use case, make at least 3 image sets in Fooocus, and write up your creative process.
Every model in the first 12 lectures learned from training data that people prepared. L13 asks a different question: when there is no right answer, only a signal of how well you did, how does a computer learn? Tsai starts from AlphaGo and Breakout and splits the field in two. Value-based methods learn a Q function that scores each action (Deep Q-Learning, TD, experience replay, ε-greedy); policy-based methods learn the action directly (policy gradient, actor-critic). The second half returns to LLMs: ChatGPT trains a reward model from human rankings and then runs RLHF with PPO, while DeepSeek has the computer check math answers automatically and uses that as the reward. Week 13 homework is the final project proposal.
The last lecture looks at two lines of technology crossing into each other. LLMs such as ChatGPT have started drawing, and the slides use early fusion plus VQ-VAE/VQGAN to explain how an image can be cut into tokens. Going the other way, Inception Labs' Mercury generates text with diffusion, noising a sentence into a row of [MASK] tokens and then restoring it. Next come a few papers anyone can use: evaluating RAG automatically, reasoning models being easier to hijack, and DeepMind's four kinds of AI risk. The lecture ends with vibe coding and a list of application tools, and the final project runs as an online conference in Gather Town.
Generative AI: Text and Image Synthesis Principles and Practice is an introductory course taught by Yen-Lung Tsai (蔡炎龍) of NCCU's Department of Mathematical Sciences and opened to other schools as a TAICA satellite course. The most complete course page online actually belongs to the Chang Gung University satellite section, where Chih-Yuan Yang is the co-teacher. This series follows Spring 2025 (semester 1132): 14 recordings, 14 slide decks, and 12 homework specs with rubrics are public, and the demo notebooks are on GitHub, so the access grade is A3. The gaps: the notebooks keep changing, submission and grading run through each school's LMS, and final projects were never published.
A guide to the BERT and its Family unit of Prof. Hung-Yu Kao's NLP course at NTHU (Fall 2025). It starts from "I record the record": Word2Vec and GloVe give both records the same vector. ELMo fixes this with a bidirectional LSTM language model. With Transformers, pretraining splits into three roads: encoders (BERT: MLM plus NSP, strong at understanding, weak at generation), encoder-decoders (T5's span corruption, BART's five noise types), and decoders (GPT: pure next-token prediction). The last part covers GPT-3's in-context learning and scaling laws, and why decoders became today's dominant backbone.
A guide to the decoding and evaluation unit in NTHU Prof. Hung-Yu Kao's NLP course (Fall 2025). The first half covers how to pick a word once the model outputs a probability distribution: greedy decoding can't take back a mistake, beam search keeps several candidates but favors short outputs, and top-k / top-p trade determinism for diversity (missing from the slides; the professor covers it verbally in class). The second half covers scoring generated text: BLEU's modified precision and brevity penalty, ROUGE-N and ROUGE-L, perplexity, and what GLUE, SQuAD 2.0, MTEB, and MMLU each measure.
A guide to the GPT-2 / T5 tutorial in NTHU Prof. Hung-Yu Kao's NLP course (Fall 2025). One task, LCSTS Chinese summarization, is solved twice. Decoder-only GPT-2 is written in native PyTorch: you join article and summary into one sequence, switch to left padding, and set padding labels to −100. Encoder-decoder mT5 uses Seq2SeqTrainer: no left padding needed, and DataCollatorForSeq2Seq handles the −100 for you. Both segment with jieba and score word-level ROUGE.
Hung-Yu Kao's Fall 2025 W8 slides walk from GPT-1 to GPT-3, explain how the Sparse Transformer behind GPT-3 cuts attention cost, and then use InstructGPT to show the gap between continuing text and following instructions. The maximum likelihood objective can't tell a fabricated fact from a slightly wrong synonym, so three extra stages are added: SFT learns how humans write, a reward model learns how humans grade, and PPO optimizes against that grade while a KL penalty keeps the model from drifting too far. The lecture closes with Llama-2: separate safety and helpfulness reward models, context distillation, and GQA for faster inference.
A guide to the Hugging Face BERT tutorial and HW3 in NTHU Prof. Hung-Yu Kao's NLP course (Fall 2025). The tutorial walks through binary IMDb sentiment classification: AutoTokenizer, the input_ids / token_type_ids / attention_mask fields, AutoModelForSequenceClassification, and Trainer. HW3 applies the same tools to SemEval 2014 Task 1: one bert-base-uncased with two heads, one regressing a 1–5 relatedness score and one classifying entailment into three classes. You add the two losses and write the training loop yourself, because Trainer is not allowed.
HW1 tests word vectors on the 19,544 Google Analogy questions (8,869 semantic, 10,675 syntactic). You first answer them with pretrained glove-wiki-gigaword-100 loaded through Gensim, then train your own Word2Vec on a 20% sample of a pre-cleaned Wikipedia dump, and plot t-SNE for the family subcategory both times. Seven TODOs are worth 55%, the report 45%. Fall 2026 keeps the same TODOs but asks for an .ipynb with outputs.
Week 1 of Hung-Yu Kao's NLP course at NTHU is a 91-page deck, W1_NLP_brief. It opens with 'Watch for kids', five readings of the telescope sentence, and a Chinese tongue-twister about eleven uncles to show why language is hard. Then, from an information-retrieval angle, it builds one pipeline: inverted index, tokenization, stemming, TF-IDF, BM25. The second half hits that pipeline's dead ends (synonyms, polysemy, vocabulary mismatch), moves to SVD-based LSA, and closes with a preview of dense vectors through Skip-gram, GloVe, and FastText.
NTHU Professor Hung-Yu Kao's Natural Language Processing covers one line in a semester: turn text into numbers (TF-IDF, word vectors), handle sequences (RNNs, attention, Transformers, sub-words), pretrained models (BERT, T5, GPT, decoding and evaluation), make models follow instructions and fine-tune with few parameters (RLHF, PEFT), then plug in outside knowledge (RAG) and reasoning. Fall 2025 slides, 32 recordings, 4 assignments, and starter notebooks are all public on GitHub; lectures are in Mandarin; there are no public solutions.
This 34-slide TA session answers a practical question. Pasting data into the ChatGPT web page one row at a time is slow and hits hourly limits, so research and homework should use the API. The notebook runs one SemEval 2014 entailment example through Gemini, Claude, and OpenAI in turn: prompts live in prompts.yaml, output is forced into JSON, then few-shot and token counting. The material is from 2024. The slide cover says 2024/11/21, and the notebook uses gemini-1.5-pro, gpt-4o, and claude-3-5-sonnet-20241022. That Claude model was retired on 2025-10-28, and Google's old Gemini SDK reached end of support on 2025-11-30.
Hung-Yu Kao's Fall 2025 PEFT slides open with a budget: full fine-tuning of Llama 2-7B in 16-bit needs about 56GB of GPU memory, while training only 0.2M parameters brings it down to about 17GB, because gradients and optimizer states nearly vanish. Intrinsic dimensionality then explains why tuning a small slice is enough: the longer a model is pretrained and the larger it is, the fewer effective dimensions fine-tuning needs. Methods fall into additive (Adapters, Prompt Tuning), selective (BitFit), reparametrization (LoRA), and hybrid (MAM Adapters, S4). The second half runs from GPT-2's task descriptions and verbalizers to the trade-offs between prefix tuning and soft prompt tuning.
HW2 treats expressions like "14*(43+20)=882" as character sequences and asks a two-layer LSTM to generate the answer one character at a time after it sees "=". The training set has 2,369,250 rows and the eval set 263,250, with every number in 0–49. Six TODOs run from building a vocabulary and batching with loss only after "=" to a generator, teacher-forced training, and exact-match evaluation. The W4 PyTorch TA session is the toolbox for it.
Each of the two RAG TA sessions builds one version. The first installs Ollama on Colab to run llama3.2:1b and wires up a minimal RAG with LangChain's Chroma, MMR, and retrieval chain. The second uses LangChain only for data prep and writes the rest by hand: chunking, text and vector stores, hybrid BM25 + cosine retrieval merged with RRF, then generation with Llama-3.2-1B-Instruct. HW4 applies the first session's skeleton to 150 cat facts and 150 GPT-5-generated QA pairs. The generator must be Llama3.2-1b and the embedding model jina-embeddings-v2-base-en, and you report recall@1, recall@5, and exact match. Code is 45% of the grade and the report 55%; the report analyzes how prompts, data format, document order, and counterfactual information change the results.
The second half of W11_RAG.pdf starts at the "From Retrievers to QA" slide and turns a retriever plus a reader into a full QA system. It begins with ORQA and REALM (2019–2020), where BERT is the reader, then covers the first paper named RAG and REPLUG, which keeps the LLM frozen. A single table then sorts seven recent fixes into three groups: rewrite the query (Query Rewriting, HyDE), make the generator robust to noise (RetRobust, RAFT, RAAT), and decide when to retrieve (FLARE, Self-RAG). It ends with noise types, four abilities an LLM needs inside RAG, and generative retrieval (GR) with reliable response generation (RRG). On the recording side, the W11 Thursday lecture stops at the RAG paper; I could not find a recording that covers the later slides.
An LLM will confidently answer that Oppenheimer was born in 1967 (the year he died). RAG retrieves first and generates second. The first 60 pages of Hung-Yu Kao's Fall 2025 RAG deck are all about finding the right material. Sparse vectors (bag-of-words, TF-IDF, BM25) are cheap and dependable; dense vectors catch paraphrases. BERT's [CLS] isn't a good sentence vector as-is, hence Sentence-BERT pooling and bi-encoders. A cross-encoder is accurate but needs nearly 50 million passes for 10,000 sentences, while a bi-encoder needs 20,000. SimCSE uses dropout as data augmentation, DPR beats BM25 with only 1,000 training examples, and GTR shows that scaling up a dual encoder improves out-of-domain retrieval.
"Look over there" is 3 tokens, the Chinese version 4, the Japanese version 12. When output length does not track input length, the classifier trick of adding an FFN at the end fails. This 33-slide deck starts from encoder-decoder models, works through vanishing gradients in RNNs and the three LSTM gates, and ends with attention fixing both long-range memory and parallelism, including why scores are divided by √d.
A guide to the Sub-word Tokenization unit of Prof. Hung-Yu Kao's NLP course at NTHU (Fall 2025). Splitting on white space only works for Western languages and cannot handle unseen words or German-style compounds. BPE starts from characters and repeatedly merges the most frequent adjacent pair into the vocabulary, so each merge adds one entry; its weakness is that the greedy split is not always the best one. The Unigram LM picks splits by probability and can even sample different ones. The slides give vocabulary sizes of 30522 for BERT, 50257 for GPT-2/GPT-3 and 32,128 for T5.