Transformers for Natural Language Processing and Computer Vision: Explore Generative AI and Large Language Models with Hugging Face, ChatGPT, GPT-4V, and DALL-E 3
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Birmingham
Packt Publishing, Limited
2024
|
| Ausgabe: | 3rd ed |
| Schlagwörter: | |
| Links: | https://portal.igpublish.com/iglibrary/search/PACKT0007072.html https://ebookcentral.proquest.com/lib/hwr/detail.action?docID=31196765 https://portal.igpublish.com/iglibrary/search/PACKT0007072.html https://portal.igpublish.com/iglibrary/search/PACKT0007072.html https://portal.igpublish.com/iglibrary/search/PACKT0007072.html |
| Umfang: | 1 Online-Ressource (xxxii, 693 Seiten) |
| ISBN: | 9781805123743 |
Internformat
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| 245 | 1 | 0 | |a Transformers for Natural Language Processing and Computer Vision |b Explore Generative AI and Large Language Models with Hugging Face, ChatGPT, GPT-4V, and DALL-E 3 |
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Datensatz im Suchindex
| DE-BY-TUM_katkey | 2839401 |
|---|---|
| _version_ | 1824079657007316992 |
| adam_text | |
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| author | Rothman, Denis |
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| bvnumber | BV049873815 |
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| contents | Cover -- Copyright -- Contributors -- Table of Contents -- Preface -- Chapter 1: What Are Transformers? -- How constant time complexity O(1) changed our lives forever -- O(1) attention conquers O(n) recurrent methods -- Attention layer -- Recurrent layer -- The magic of the computational time complexity of an attention layer -- Computational time complexity with a CPU -- Computational time complexity with a GPU -- Computational time complexity with a TPU -- TPU-LLM -- A brief journey from recurrent to attention -- A brief history -- From one token to an AI revolution -- From one token to everything -- Foundation Models -- From general purpose to specific tasks -- The role of AI professionals -- The future of AI professionals -- What resources should we use? -- Decision-making guidelines -- The rise of transformer seamless APIs and assistants -- Choosing ready-to-use API-driven libraries -- Choosing a cloud platform and transformer model -- Summary -- Questions -- References -- Further reading -- Chapter 2: Getting Started with the Architecture of the Transformer Model -- The rise of the Transformer: Attention Is All You Need -- The encoder stack -- Input embedding -- Positional encoding -- Sublayer 1: Multi-head attention -- Sublayer 2: Feedforward network -- The decoder stack -- Output embedding and position encoding -- The attention layers -- The FFN sublayer, the post-LN, and the linear layer -- Training and performance -- Hugging Face transformer models -- Summary -- Questions -- References -- Further reading -- Chapter 3: Emergent vs Downstream Tasks: The Unseen Depths of Transformers -- The paradigm shift: What is an NLP task? -- Inside the head of the attention sublayer of a transformer -- Exploring emergence with ChatGPT -- Investigating the potential of downstream tasks -- Evaluating models with metrics -- Accuracy score -- F1-score MCC -- Human evaluation -- Benchmark tasks and datasets -- Defining the SuperGLUE benchmark tasks -- Running downstream tasks -- The Corpus of Linguistic Acceptability (CoLA) -- Stanford Sentiment TreeBank (SST-2) -- Microsoft Research Paraphrase Corpus (MRPC) -- Winograd schemas -- Summary -- Questions -- References -- Further reading -- Chapter 4: Advancements in Translations with Google Trax, Google Translate, and Gemini -- Defining machine translation -- Human transductions and translations -- Machine transductions and translations -- Evaluating machine translations -- Preprocessing a WMT dataset -- Preprocessing the raw data -- Finalizing the preprocessing of the datasets -- Evaluating machine translations with BLEU -- Geometric evaluations -- Applying a smoothing technique -- Translations with Google Trax -- Installing Trax -- Creating the Original Transformer model -- Initializing the model using pretrained weights -- Tokenizing a sentence -- Decoding from the Transformer -- De-tokenizing and displaying the translation -- Translation with Google Translate -- Translation with a Google Translate AJAX API Wrapper -- Implementing googletrans -- Translation with Gemini -- Gemini's potential -- Summary -- Questions -- References -- Further reading -- Chapter 5: Diving into Fine-Tuning through BERT -- The architecture of BERT -- The encoder stack -- Preparing the pretraining input environment -- Pretraining and fine-tuning a BERT model -- Fine-tuning BERT -- Defining a goal -- Hardware constraints -- Installing Hugging Face Transformers -- Importing the modules -- Specifying CUDA as the device for torch -- Loading the CoLA dataset -- Creating sentences, label lists, and adding BERT tokens -- Activating the BERT tokenizer -- Processing the data -- Creating attention masks -- Splitting the data into training and validation sets Converting all the data into torch tensors -- Selecting a batch size and creating an iterator -- BERT model configuration -- Loading the Hugging Face BERT uncased base model -- Optimizer grouped parameters -- The hyperparameters for the training loop -- The training loop -- Training evaluation -- Predicting and evaluating using the holdout dataset -- Exploring the prediction process -- Evaluating using the Matthews correlation coefficient -- Matthews correlation coefficient evaluation for the whole dataset -- Building a Python interface to interact with the model -- Saving the model -- Creating an interface for the trained model -- Interacting with the model -- Summary -- Questions -- References -- Further reading -- Chapter 6: Pretraining a Transformer from Scratch through RoBERTa -- Training a tokenizer and pretraining a transformer -- Building KantaiBERT from scratch -- Step 1: Loading the dataset -- Step 2: Installing Hugging Face transformers -- Step 3: Training a tokenizer -- Step 4: Saving the files to disk -- Step 5: Loading the trained tokenizer files -- Step 6: Checking resource constraints: GPU and CUDA -- Step 7: Defining the configuration of the model -- Step 8: Reloading the tokenizer in transformers -- Step 9: Initializing a model from scratch -- Exploring the parameters -- Step 10: Building the dataset -- Step 11: Defining a data collator -- Step 12: Initializing the trainer -- Step 13: Pretraining the model -- Step 14: Saving the final model (+tokenizer + config) to disk -- Step 15: Language modeling with FillMaskPipeline -- Pretraining a Generative AI customer support model on X data -- Step 1: Downloading the dataset -- Step 2: Installing Hugging Face transformers -- Step 3: Loading and filtering the data -- Step 4: Checking Resource Constraints: GPU and CUDA -- Step 5: Defining the configuration of the model Step 6: Creating and processing the dataset -- Step 7: Initializing the trainer -- Step 8: Pretraining the model -- Step 9: Saving the model -- Step 10: User interface to chat with the Generative AI agent -- Further pretraining -- Limitations -- Next steps -- Summary -- Questions -- References -- Further reading -- Chapter 7: The Generative AI Revolution with ChatGPT -- GPTs as GPTs -- Improvement -- Diffusion -- New application sectors -- Self-service assistants -- Development assistants -- Pervasiveness -- The architecture of OpenAI GPT transformer models -- The rise of billion-parameter transformer models -- The increasing size of transformer models -- Context size and maximum path length -- From fine-tuning to zero-shot models -- Stacking decoder layers -- GPT models -- OpenAI models as assistants -- ChatGPT provides source code -- GitHub Copilot code assistant -- General-purpose prompt examples -- Getting started with ChatGPT - GPT-4 as an assistant -- 1. GPT-4 helps to explain how to write source code -- 2. GPT-4 creates a function to show the YouTube presentation of GPT-4 by Greg Brockman on March 14, 2023 -- 3. GPT-4 creates an application for WikiArt to display images -- 4. GPT-4 creates an application to display IMDb reviews -- 5. GPT-4 creates an application to display a newsfeed -- 6. GPT-4 creates a k-means clustering (KMC) algorithm -- Getting started with the GPT-4 API -- Running our first NLP task with GPT-4 -- Steps 1: Installing OpenAI and Step 2: Entering the API key -- Step 3: Running an NLP task with GPT-4 -- Key hyperparameters -- Running multiple NLP tasks -- Retrieval Augmented Generation (RAG) with GPT-4 -- Installation -- Document retrieval -- Augmented retrieval generation -- Summary -- Questions -- References -- Further reading -- Chapter 8: Fine-Tuning OpenAI GPT Models -- Risk management Fine-tuning a GPT model for completion (generative) -- 1. Preparing the dataset -- 1.1. Preparing the data in JSON -- 1.2. Converting the data to JSONL -- 2. Fine-tuning an original model -- 3. Running the fine-tuned GPT model -- 4. Managing fine-tuned jobs and models -- Before leaving -- Summary -- Questions -- References -- Further reading -- Chapter 9: Shattering the Black Box with Interpretable Tools -- Transformer visualization with BertViz -- Running BertViz -- Step 1: Installing BertViz and importing the modules -- Step 2: Load the models and retrieve attention -- Step 3: Head view -- Step 4: Processing and displaying attention heads -- Step 5: Model view -- Step 6: Displaying the output probabilities of attention heads -- Streaming the output of the attention heads -- Visualizing word relationships using attention scores with pandas -- exBERT -- Interpreting Hugging Face transformers with SHAP -- Introducing SHAP -- Explaining Hugging Face outputs with SHAP -- Transformer visualization via dictionary learning -- Transformer factors -- Introducing LIME -- The visualization interface -- Other interpretable AI tools -- LIT -- PCA -- Running LIT -- OpenAI LLMs explain neurons in transformers -- Limitations and human control -- Summary -- Questions -- References -- Further reading -- Chapter 10: Investigating the Role of Tokenizers in Shaping Transformer Models -- Matching datasets and tokenizers -- Best practices -- Step 1: Preprocessing -- Step 2: Quality control -- Step 3: Continuous human quality control -- Word2Vec tokenization -- Case 0: Words in the dataset and the dictionary -- Case 1: Words not in the dataset or the dictionary -- Case 2: Noisy relationships -- Case 3: Words in a text but not in the dictionary -- Case 4: Rare words -- Case 5: Replacing rare words Exploring sentence and WordPiece tokenizers to understand the efficiency of subword tokenizers for transformers |
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| dewey-hundreds | 600 - Technology (Applied sciences) |
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| discipline | Wirtschaftswissenschaften |
| edition | 3rd ed |
| format | Electronic eBook |
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| spellingShingle | Rothman, Denis Transformers for Natural Language Processing and Computer Vision Explore Generative AI and Large Language Models with Hugging Face, ChatGPT, GPT-4V, and DALL-E 3 Cover -- Copyright -- Contributors -- Table of Contents -- Preface -- Chapter 1: What Are Transformers? -- How constant time complexity O(1) changed our lives forever -- O(1) attention conquers O(n) recurrent methods -- Attention layer -- Recurrent layer -- The magic of the computational time complexity of an attention layer -- Computational time complexity with a CPU -- Computational time complexity with a GPU -- Computational time complexity with a TPU -- TPU-LLM -- A brief journey from recurrent to attention -- A brief history -- From one token to an AI revolution -- From one token to everything -- Foundation Models -- From general purpose to specific tasks -- The role of AI professionals -- The future of AI professionals -- What resources should we use? -- Decision-making guidelines -- The rise of transformer seamless APIs and assistants -- Choosing ready-to-use API-driven libraries -- Choosing a cloud platform and transformer model -- Summary -- Questions -- References -- Further reading -- Chapter 2: Getting Started with the Architecture of the Transformer Model -- The rise of the Transformer: Attention Is All You Need -- The encoder stack -- Input embedding -- Positional encoding -- Sublayer 1: Multi-head attention -- Sublayer 2: Feedforward network -- The decoder stack -- Output embedding and position encoding -- The attention layers -- The FFN sublayer, the post-LN, and the linear layer -- Training and performance -- Hugging Face transformer models -- Summary -- Questions -- References -- Further reading -- Chapter 3: Emergent vs Downstream Tasks: The Unseen Depths of Transformers -- The paradigm shift: What is an NLP task? -- Inside the head of the attention sublayer of a transformer -- Exploring emergence with ChatGPT -- Investigating the potential of downstream tasks -- Evaluating models with metrics -- Accuracy score -- F1-score MCC -- Human evaluation -- Benchmark tasks and datasets -- Defining the SuperGLUE benchmark tasks -- Running downstream tasks -- The Corpus of Linguistic Acceptability (CoLA) -- Stanford Sentiment TreeBank (SST-2) -- Microsoft Research Paraphrase Corpus (MRPC) -- Winograd schemas -- Summary -- Questions -- References -- Further reading -- Chapter 4: Advancements in Translations with Google Trax, Google Translate, and Gemini -- Defining machine translation -- Human transductions and translations -- Machine transductions and translations -- Evaluating machine translations -- Preprocessing a WMT dataset -- Preprocessing the raw data -- Finalizing the preprocessing of the datasets -- Evaluating machine translations with BLEU -- Geometric evaluations -- Applying a smoothing technique -- Translations with Google Trax -- Installing Trax -- Creating the Original Transformer model -- Initializing the model using pretrained weights -- Tokenizing a sentence -- Decoding from the Transformer -- De-tokenizing and displaying the translation -- Translation with Google Translate -- Translation with a Google Translate AJAX API Wrapper -- Implementing googletrans -- Translation with Gemini -- Gemini's potential -- Summary -- Questions -- References -- Further reading -- Chapter 5: Diving into Fine-Tuning through BERT -- The architecture of BERT -- The encoder stack -- Preparing the pretraining input environment -- Pretraining and fine-tuning a BERT model -- Fine-tuning BERT -- Defining a goal -- Hardware constraints -- Installing Hugging Face Transformers -- Importing the modules -- Specifying CUDA as the device for torch -- Loading the CoLA dataset -- Creating sentences, label lists, and adding BERT tokens -- Activating the BERT tokenizer -- Processing the data -- Creating attention masks -- Splitting the data into training and validation sets Converting all the data into torch tensors -- Selecting a batch size and creating an iterator -- BERT model configuration -- Loading the Hugging Face BERT uncased base model -- Optimizer grouped parameters -- The hyperparameters for the training loop -- The training loop -- Training evaluation -- Predicting and evaluating using the holdout dataset -- Exploring the prediction process -- Evaluating using the Matthews correlation coefficient -- Matthews correlation coefficient evaluation for the whole dataset -- Building a Python interface to interact with the model -- Saving the model -- Creating an interface for the trained model -- Interacting with the model -- Summary -- Questions -- References -- Further reading -- Chapter 6: Pretraining a Transformer from Scratch through RoBERTa -- Training a tokenizer and pretraining a transformer -- Building KantaiBERT from scratch -- Step 1: Loading the dataset -- Step 2: Installing Hugging Face transformers -- Step 3: Training a tokenizer -- Step 4: Saving the files to disk -- Step 5: Loading the trained tokenizer files -- Step 6: Checking resource constraints: GPU and CUDA -- Step 7: Defining the configuration of the model -- Step 8: Reloading the tokenizer in transformers -- Step 9: Initializing a model from scratch -- Exploring the parameters -- Step 10: Building the dataset -- Step 11: Defining a data collator -- Step 12: Initializing the trainer -- Step 13: Pretraining the model -- Step 14: Saving the final model (+tokenizer + config) to disk -- Step 15: Language modeling with FillMaskPipeline -- Pretraining a Generative AI customer support model on X data -- Step 1: Downloading the dataset -- Step 2: Installing Hugging Face transformers -- Step 3: Loading and filtering the data -- Step 4: Checking Resource Constraints: GPU and CUDA -- Step 5: Defining the configuration of the model Step 6: Creating and processing the dataset -- Step 7: Initializing the trainer -- Step 8: Pretraining the model -- Step 9: Saving the model -- Step 10: User interface to chat with the Generative AI agent -- Further pretraining -- Limitations -- Next steps -- Summary -- Questions -- References -- Further reading -- Chapter 7: The Generative AI Revolution with ChatGPT -- GPTs as GPTs -- Improvement -- Diffusion -- New application sectors -- Self-service assistants -- Development assistants -- Pervasiveness -- The architecture of OpenAI GPT transformer models -- The rise of billion-parameter transformer models -- The increasing size of transformer models -- Context size and maximum path length -- From fine-tuning to zero-shot models -- Stacking decoder layers -- GPT models -- OpenAI models as assistants -- ChatGPT provides source code -- GitHub Copilot code assistant -- General-purpose prompt examples -- Getting started with ChatGPT - GPT-4 as an assistant -- 1. GPT-4 helps to explain how to write source code -- 2. GPT-4 creates a function to show the YouTube presentation of GPT-4 by Greg Brockman on March 14, 2023 -- 3. GPT-4 creates an application for WikiArt to display images -- 4. GPT-4 creates an application to display IMDb reviews -- 5. GPT-4 creates an application to display a newsfeed -- 6. GPT-4 creates a k-means clustering (KMC) algorithm -- Getting started with the GPT-4 API -- Running our first NLP task with GPT-4 -- Steps 1: Installing OpenAI and Step 2: Entering the API key -- Step 3: Running an NLP task with GPT-4 -- Key hyperparameters -- Running multiple NLP tasks -- Retrieval Augmented Generation (RAG) with GPT-4 -- Installation -- Document retrieval -- Augmented retrieval generation -- Summary -- Questions -- References -- Further reading -- Chapter 8: Fine-Tuning OpenAI GPT Models -- Risk management Fine-tuning a GPT model for completion (generative) -- 1. Preparing the dataset -- 1.1. Preparing the data in JSON -- 1.2. Converting the data to JSONL -- 2. Fine-tuning an original model -- 3. Running the fine-tuned GPT model -- 4. Managing fine-tuned jobs and models -- Before leaving -- Summary -- Questions -- References -- Further reading -- Chapter 9: Shattering the Black Box with Interpretable Tools -- Transformer visualization with BertViz -- Running BertViz -- Step 1: Installing BertViz and importing the modules -- Step 2: Load the models and retrieve attention -- Step 3: Head view -- Step 4: Processing and displaying attention heads -- Step 5: Model view -- Step 6: Displaying the output probabilities of attention heads -- Streaming the output of the attention heads -- Visualizing word relationships using attention scores with pandas -- exBERT -- Interpreting Hugging Face transformers with SHAP -- Introducing SHAP -- Explaining Hugging Face outputs with SHAP -- Transformer visualization via dictionary learning -- Transformer factors -- Introducing LIME -- The visualization interface -- Other interpretable AI tools -- LIT -- PCA -- Running LIT -- OpenAI LLMs explain neurons in transformers -- Limitations and human control -- Summary -- Questions -- References -- Further reading -- Chapter 10: Investigating the Role of Tokenizers in Shaping Transformer Models -- Matching datasets and tokenizers -- Best practices -- Step 1: Preprocessing -- Step 2: Quality control -- Step 3: Continuous human quality control -- Word2Vec tokenization -- Case 0: Words in the dataset and the dictionary -- Case 1: Words not in the dataset or the dictionary -- Case 2: Noisy relationships -- Case 3: Words in a text but not in the dictionary -- Case 4: Rare words -- Case 5: Replacing rare words Exploring sentence and WordPiece tokenizers to understand the efficiency of subword tokenizers for transformers ChatGPT. Artificial intelligence-Data processing Cloud computing |
| title | Transformers for Natural Language Processing and Computer Vision Explore Generative AI and Large Language Models with Hugging Face, ChatGPT, GPT-4V, and DALL-E 3 |
| title_auth | Transformers for Natural Language Processing and Computer Vision Explore Generative AI and Large Language Models with Hugging Face, ChatGPT, GPT-4V, and DALL-E 3 |
| title_exact_search | Transformers for Natural Language Processing and Computer Vision Explore Generative AI and Large Language Models with Hugging Face, ChatGPT, GPT-4V, and DALL-E 3 |
| title_full | Transformers for Natural Language Processing and Computer Vision Explore Generative AI and Large Language Models with Hugging Face, ChatGPT, GPT-4V, and DALL-E 3 |
| title_fullStr | Transformers for Natural Language Processing and Computer Vision Explore Generative AI and Large Language Models with Hugging Face, ChatGPT, GPT-4V, and DALL-E 3 |
| title_full_unstemmed | Transformers for Natural Language Processing and Computer Vision Explore Generative AI and Large Language Models with Hugging Face, ChatGPT, GPT-4V, and DALL-E 3 |
| title_short | Transformers for Natural Language Processing and Computer Vision |
| title_sort | transformers for natural language processing and computer vision explore generative ai and large language models with hugging face chatgpt gpt 4v and dall e 3 |
| title_sub | Explore Generative AI and Large Language Models with Hugging Face, ChatGPT, GPT-4V, and DALL-E 3 |
| topic | ChatGPT. Artificial intelligence-Data processing Cloud computing |
| topic_facet | ChatGPT. Artificial intelligence-Data processing Cloud computing |
| url | https://portal.igpublish.com/iglibrary/search/PACKT0007072.html |
| work_keys_str_mv | AT rothmandenis transformersfornaturallanguageprocessingandcomputervisionexploregenerativeaiandlargelanguagemodelswithhuggingfacechatgptgpt4vanddalle3 |