Tag: Transformers

Price: $39.99
(as of Dec 28,2024 04:06:29 UTC – Details)

From the Publisher

Book overview

This 250-page book contains ~600 intuitive and colored illustrations along with practical examples to deeply understand concepts related to Transformers & Large Language Models.

The parts below show a glimpse of what this book has to offer.

Target audience

Students
Researchers
Job seekers
Machine Learning practitioners
Industry leaders

Deep Learning fundamentals & Embeddings

Foundations

Neural networks: input, hidden layer, output, softmax layers
Training: parameter learning, optimizers (including Adam and AdamW), loss functions (cross-entropy, KL divergence and others), regularization (dropout, early stopping, weight regularization)
Evaluation: data splits, metrics (confusion matrix and common metrics), bias-variance trade-off

Embeddings

Tokenization: tokenizer, vocabulary, BPE, Unigram, encoding, decoding
Token embeddings: word2vec (CBOW, skip-gram), GloVE
Document embeddings: bag of words, recurrent neural networks, GRU, LSTM, ELMo
Embedding operations: cosine similarity, t-SNE, locality-sensitive hashing

Transformers

Transformer architecture & extensions

Self-attention mechanism: query key, value, multi-head attention
Transformer model walkthrough and detailed example
Encoder-only models (BERT), decoder-only models (GPT) and encoder-decoder models (T5)

Tricks to optimize complexity & interpretability

Sparse attention: Reformer
Low rank attention: Linformer, Performer, Longformer
Hardware optimization: flash attention
Attention maps, TCAV, integrated gradients, LIME, TracIn

Large Language Models & Applications

Large language models

Pretraining: data mixtures, training objective
Prompt engineering: context window, token sampling, in-context learning, chain of thought, ReAct, injection, model hallucination
Finetuning: SFT, PEFT methods (LoRA, Prefix Tuning, Adapters)
Preference tuning: RLHF (Reward modeling, reinforcement learning), DPO (supervised approach and variants such as IPO)

Applications

Retrieval augmentation: RAG including retriever, generator and relevant hyperparameters
Machine translation, summarization, sentiment extraction and others
Metrics include BLEU, WER, ROUGE, METEOR

ASIN ‏ : ‎ B0DC4NYLTN
Publisher ‏ : ‎ Independently published (August 3, 2024)
Language ‏ : ‎ English
Paperback ‏ : ‎ 247 pages
ISBN-13 ‏ : ‎ 979-8836693312
Item Weight ‏ : ‎ 13.7 ounces
Dimensions ‏ : ‎ 6 x 0.58 x 9 inches

Are you looking to become a pro at Transformers and Large Language Models? Look no further! Our Super Study Guide has got you covered with all the essential information you need to know about these powerful tools in the field of natural language processing.

In this comprehensive guide, we break down the fundamentals of Transformers and Large Language Models, discussing their architecture, training process, and applications in various NLP tasks. We also provide tips and tricks for maximizing the performance of these models and staying up-to-date with the latest advancements in the field.

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