Transformers for machine learning : (Record no. 5955)
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| fixed length control field | 10112cam a2200577 i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 9781003170082 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | FlBoTFG |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240213122832.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS | |
| fixed length control field | m o d |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr cnu|||unuuu |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220412s2022 xx eo 000 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | OCoLC-P |
| Language of cataloging | eng |
| Description conventions | rda |
| -- | pn |
| Transcribing agency | OCoLC-P |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781003170082 |
| Qualifying information | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 1003170080 |
| Qualifying information | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781000587074 |
| Qualifying information | (electronic bk. ; |
| -- | PDF) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 100058707X |
| Qualifying information | (electronic bk. ; |
| -- | PDF) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781000587098 |
| Qualifying information | (electronic bk. ; |
| -- | EPUB) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 1000587096 |
| Qualifying information | (electronic bk. ; |
| -- | EPUB) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Canceled/invalid ISBN | 9780367771652 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Canceled/invalid ISBN | 9780367767341 |
| 024 7# - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 10.1201/9781003170082 |
| Source of number or code | doi |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (OCoLC)1310470794 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (OCoLC-P)1310470794 |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | QA76.87 |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | COM |
| Subject category code subdivision | 044000 |
| Source | bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | COM |
| Subject category code subdivision | 042000 |
| Source | bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | COM |
| Subject category code subdivision | 016000 |
| Source | bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | UYQ |
| Source | bicssc |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.3/2 |
| Edition number | 23/eng/20220218 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Kamath, Uday, |
| Relator term | author. |
| 245 10 - TITLE STATEMENT | |
| Title | Transformers for machine learning : |
| Remainder of title | a deep dive / |
| Statement of responsibility, etc. | Uday Kamath, Kenneth Graham, Wael Emara. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | First edition. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | [Place of publication not identified] : |
| Name of producer, publisher, distributor, manufacturer | Chapman and Hall/CRC, |
| Date of production, publication, distribution, manufacture, or copyright notice | 2022. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 online resource (xxvi, 257 pages) |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | computer |
| Media type code | c |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | online resource |
| Carrier type code | cr |
| Source | rdacarrier |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | List of Figures List of Tables Author Bios Foreword Preface Contributors Deep Learning and Transformers: An Introduction 1.1 DEEP LEARNING: A HISTORIC PERSPECTIVE1.2 TRANSFORMERS AND TAXONOMY 1.2.1 Modified Transformer Architecture 1.2.1.1 Transformer block changes 1.2.1.2 Transformer sublayer changes 1.2.2 Pretraining Methods and Applications 1.3 RESOURCES 1.3.1 Libraries and Implementations 1.3.2 Books 1.3.3 Courses, Tutorials, and Lectures 1.3.4 Case Studies and Details Transformers: Basics and Introduction 2.1 ENCODER-DECODER ARCHITECTURE 2.2 SEQUENCE TO SEQUENCE 2.2.1 Encoder 2.2.2 Decoder 2.2.3 Training 2.2.4 Issues with RNN-based Encoder Decoder 2.3 ATTENTION MECHANISM 2.3.1 Background 2.3.2 Types of Score-Based Attention 2.3.2.1 Dot Product (multiplicative) 2.3.2.2 Scaled Dot Product or multiplicative 2.3.2.3 Linear, MLP, or additive 2.3.3 Attention-based Sequence to Sequence 2.4 TRANSFORMER 2.4.1 Source and Target Representation 2.4.1.1 Word Embedding 2.4.1.2 Positional Encoding 2.4.2 Attention Layers 2.4.2.1 Self-Attention 2.4.2.2 Multi-Head Attention 2.4.2.3 Masked Multi-Head Attention 2.4.2.4 Encoder-Decoder Multi-Head Attention 2.4.3 Residuals and Layer Normalization 2.4.4 Position-wise Feed-Forward Networks 2.4.5 Encoder 2.4.6 Decoder 2.5 CASE STUDY: MACHINE TRANSLATION 2.5.1 Goal 2.5.2 Data, Tools and Libraries 2.5.3 Experiments, Results and Analysis 2.5.3.1 Exploratory Data Analysis 2.5.3.2 Attention 2.5.3.3 Transformer2.5.3.4 Results and Analysis 2.5.3.5 Explainability Bidirectional Encoder Representations from Transformers (BERT) 3.1 BERT 3.1.1 Architecture 3.1.2 Pre-training 3.1.3 Fine-tuning 3.2 BERT VARIANTS 3.2.1 RoBERTa 3.3 APPLICATIONS 3.3.1 TaBERT 3.3.2 BERTopic 3.4 BERT INSIGHTS 3.4.1 BERT Sentence Representation 3.4.2 BERTology 3.5 CASE STUDY: TOPIC MODELING WITH TRANSFORMERS 3.5.1 Goal 3.5.2 Data, Tools, and Libraries 3.5.2.1 Data 3.5.2.2 Compute embeddings 3.5.3 Experiments, Results, and Analysis 3.5.3.1 Building Topics 3.5.3.2 Topic size distribution 3.5.3.3 Visualization of topics 3.5.3.4 Content of topics 3.6 CASE STUDY: FINE-TUNING BERT 3.6.1 Goal 3.6.2 Data, Tools and Libraries 3.6.3 Experiments, Results and Analysis Multilingual Transformer Architectures 4.1 MULTILINGUAL TRANSFORMER ARCHITECTURES 4.1.1 Basic Multilingual Transformer 4.1.2 Single-Encoder Multilingual NLU 4.1.2.1 mBERT 4.1.2.2 XLM 4.1.2.3 XLM-RoBERTa 4.1.2.4 ALM 4.1.2.5 Unicoder 4.1.2.6 INFOXL4.1.2.7 AMBER 4.1.2.8 ERNIE-M 4.1.2.9 HITCL 4.1.3 Dual-Encoder Multilingual NLU 4.1.3.1 LaBSE 4.1.3.2 mUSE 4.1.4 Multilingual NLG 4.2 MULTILINGUAL DATA 4.2.1 Pre-training Data 4.2.2 Multilingual Benchmarks 4.2.2.1 Classification 4.2.2.2 Structure Prediction 4.2.2.3 Question Answering 4.2.2.4 Semantic Retrieval 4.3 MULTILINGUAL TRANSFER LEARNING INSIGHTS 4.3.1 Zero-shot Cross-lingual Learning 4.3.1.1 Data Factors 4.3.1.2 Model Architecture Factors 4.3.1.3 Model Tasks Factors 4.3.2 Language-agnostic Cross-lingual Representations4.4 CASE STUDY 4.4.1 Goal 4.4.2 Data, Tools, and Libraries 4.4.3 Experiments, Results, and Analysis 4.4.3.1 Data Preprocessing 4.4.3.2 Experiments Transformer Modifications5.1 TRANSFORMER BLOCK MODIFICATIONS 5.1.1 Lightweight Transformers 5.1.1.1 Funnel-Transformer 5.1.1.2 DeLighT 5.1.2 Connections between Transformer Blocks 5.1.2.1 RealFormer 5.1.3 Adaptive Computation Time 5.1.3.1 Universal Transformers (UT) 5.1.4 Recurrence Relations between Transformer Blocks 5.1.4.1 Transformer-XL 5.1.5 Hierarchical Transformers 5.2 TRANSFORMERS WITH MODIFIED MULTI-HEAD SELF-ATTENTION5.2.1 Structure of Multi-head Self-Attention 5.2.1.1 Multi-head self-attention 5.2.1.2 Space and time complexity 5.2.2 Reducing Complexity of Self-attention 5.2.2.1 Longformer 5.2.2.2 Reformer 5.2.2.3 Performer 5.2.2.4 Big Bird 5.2.3 Improving Multi-head-attention 5.2.3.1 Talking-Heads Attention 5.2.4 Biasing Attention with Priors 5.2.5 Prototype Queries5.2.5.1 Clustered Attention 5.2.6 Compressed Key-Value Memory 5.2.6.1 Luna: Linear Unified Nested Attention 5.2.7 Low-rank Approximations5.2.7.1 Linformer 5.3 MODIFICATIONS FOR TRAINING TASK EFFICIENCY 5.3.1 ELECTRA5.3.1.1 Replaced token detection 5.3.2 T5 5.4 TRANSFORMER SUBMODULE CHANGES 5.4.1 Switch Transformer 5.5 CASE STUDY: SENTIMENT ANALYSIS5.5.1 Goal 5.5.2 Data, Tools, and Libraries 5.5.3 Experiments, Results, and Analysis 5.5.3.1 Visualizing attention head weights 5.5.3.2 Analysis Pretrained and Application-Specific Transformers 6.1 TEXT PROCESSING 6.1.1 Domain-Specific Transformers 6.1.1.1 BioBERT 6.1.1.2 SciBERT 6.1.1.3 FinBERT 6.1.2 Text-to-text Transformers 6.1.2.1 ByT5 6.1.3 Text generation 6.1.3.1 GPT: Generative Pre-training 6.1.3.2 GPT-2 6.1.3.3 GPT-3 6.2 COMPUTER VISION 6.2.1 Vision Transformer 6.3 AUTOMATIC SPEECH RECOGNITION 6.3.1 Wav2vec 2.0 6.3.2 Speech2Text2 6.3.3 HuBERT: Hidden Units BERT 6.4 MULTIMODAL AND MULTITASKING TRANSFORMER 6.4.1 Vision-and-Language BERT (VilBERT) 6.4.2 Unified Transformer (UniT) 6.5 VIDEO PROCESSING WITH TIMESFORMER 6.5.1 Patch embeddings 6.5.2 Self-attention 6.5.2.1 Spatiotemporal self-attention 6.5.2.2 Spatiotemporal attention blocks 6.6 GRAPH TRANSFORMERS 6.6.1 Positional encodings in a graph 6.6.1.1 Laplacian positional encodings 6.6.2 Graph transformer input 6.6.2.1 Graphs without edge attributes 6.6.2.2 Graphs with edge attributes 6.7 REINFORCEMENT LEARNING 6.7.1 Decision Transformer 6.8 CASE STUDY: AUTOMATIC SPEECH RECOGNITION 6.8.1 Goal 6.8.2 Data, Tools, and Libraries 6.8.3 Experiments, Results, and Analysis 6.8.3.1 Preprocessing speech data 6.8.3.2 Evaluation Interpretability and Explainability Techniques for Transformers7.1 TRAITS OF EXPLAINABLE SYSTEMS 7.2 RELATED AREAS THAT IMPACT EXPLAINABILITY 7.3 EXPLAINABLE METHODS TAXONOMY 7.3.1 Visualization Methods 7.3.1.1 Backpropagation-based 7.3.1.2 Perturbation-based 7.3.2 Model Distillation 7.3.2.1 Local Approximation 7.3.2.2 Model Translation 7.3.3 Intrinsic Methods 7.3.3.1 Probing Mechanism 7.3.3.2 Joint Training 7.4 ATTENTION AND EXPLANATION 7.4.1 Attention is not Explanation 7.4.1.1 Attention Weights and Feature Importance 7.4.1.2 Counterfactual Experiments 7.4.2 Attention is not not Explanation 7.4.2.1 Is attention necessary for all tasks? 7.4.2.2 Searching for Adversarial Models 7.4.2.3 Attention Probing 7.5 QUANTIFYING ATTENTION FLOW 7.5.1 Information flow as DAG 7.5.2 Attention Rollout 7.5.3 Attention Flow 7.6 CASE STUDY: TEXT CLASSIFICATION WITH EXPLAINABILITY 7.6.1 Goal 7.6.2 Data, Tools, and Libraries 7.6.3 Experiments, Results and Analysis 7.6.3.1 Exploratory Data Analysis 7.6.3.2 Experiments 7.6.3.3 Error Analysis and Explainability Bibliography Alphabetical Index |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Transformers are becoming a core part of many neural network architectures, employed in a wide range of applications such as NLP, Speech Recognition, Time Series, and Computer Vision. Transformers have gone through many adaptations and alterations, resulting in newer techniques and methods. Transformers for Machine Learning: A Deep Dive is the first comprehensive book on transformers. Key Features: A comprehensive reference book for detailed explanations for every algorithm and techniques related to the transformers. 60+ transformer architectures covered in a comprehensive manner. A book for understanding how to apply the transformer techniques in speech, text, time series, and computer vision. Practical tips and tricks for each architecture and how to use it in the real world. Hands-on case studies and code snippets for theory and practical real-world analysis using the tools and libraries, all ready to run in Google Colab. The theoretical explanations of the state-of-the-art transformer architectures will appeal to postgraduate students and researchers (academic and industry) as it will provide a single entry point with deep discussions of a quickly moving field. The practical hands-on case studies and code will appeal to undergraduate students, practitioners, and professionals as it allows for quick experimentation and lowers the barrier to entry into the field. |
| 588 ## - SOURCE OF DESCRIPTION NOTE | |
| Source of description note | OCLC-licensed vendor bibliographic record. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Neural networks (Computer science) |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Computational intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Machine learning. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | COMPUTERS |
| General subdivision | Neural Networks. |
| Source of heading or term | bisacsh |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | COMPUTERS |
| General subdivision | Natural Language Processing. |
| Source of heading or term | bisacsh |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | COMPUTERS |
| General subdivision | Computer Vision & Pattern Recognition. |
| Source of heading or term | bisacsh |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Graham, Kenneth L. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Emara, Wael |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Materials specified | Taylor & Francis |
| Uniform Resource Identifier | <a href="https://www.taylorfrancis.com/books/e/9781003170082">https://www.taylorfrancis.com/books/e/9781003170082</a> |
| 856 42 - ELECTRONIC LOCATION AND ACCESS | |
| Materials specified | OCLC metadata license agreement |
| Uniform Resource Identifier | <a href="http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf">http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf</a> |
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