{"product_id":"138563","title":"A Complete Guide to Using Transformer with PyTorch in 10 Easy Projects ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jXPCfGO29iWyYK1IrpH4sr19wX1Cl.png?v=1765064513\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e A Complete Guide to Using Transformer with PyTorch in 10 Easy Projects \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/142636957\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cdiv\u003e \u003cb\u003eMastering step-by-step projects\u003cbr\u003e How to use the Hugging Face Transformer!\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e You can experience a balanced approach that covers not only the basic theory of transformers but also practical application implementation through 10 projects. \u003cbr\u003eWe'll guide you through the core processes of Transformer, including natural language processing, computer vision, speech recognition, reinforcement learning, and multimodality, as well as various machine learning\/deep learning tasks.\u003cbr\u003e The introduction explains the inner workings of the transformer architecture and its main models, while subsequent chapters cover pretraining, fine-tuning, and practical examples of open-source models.\u003cbr\u003e In particular, it provides separate chapters on the HuggingFace ecosystem, transfer learning, model deployment, and serving to help even beginners learn about the Transformer model easily. It also includes best practices and debugging guidance for Transformer models using PyTorch and HuggingFace for practitioners.\u003cbr\u003e\n\u003c\/div\u003e\u003c\/div\u003e\u003c\/div\u003e \",\"\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eChapter 1 Transformer Architecture\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _01.1 NLP Model Development History\u003cbr\u003e __01.1.1 Recurrent Neural Network (RNN)\u003cbr\u003e __01.1.2 LSTM\u003cbr\u003e __01.1.3 RNN Encoder-Decoder\u003cbr\u003e __01.1.4 Attention Mechanism\u003cbr\u003e _01.2 Transformer Architecture\u003cbr\u003e __01.2.1 Embedding\u003cbr\u003e __01.2.2 Positional Encoding \u003cbr\u003e__01.2.3 Model input\u003cbr\u003e __01.2.4 Encoder layer\u003cbr\u003e __01.2.5 Attention Mechanism\u003cbr\u003e _01.3 Transformer Learning Process\u003cbr\u003e _01.4 Transformer Inference Process\u003cbr\u003e _01.5 Transformer Types and Applications\u003cbr\u003e __01.5.1 Encoder-only model\u003cbr\u003e __01.5.2 Decoder-only model\u003cbr\u003e __01.5.3 Encoder-Decoder Model\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 2: The Hugging Face Ecosystem\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _02.1 Hugging Face Overview\u003cbr\u003e __02.1.1 Main Components\u003cbr\u003e __02.1.2 Tokenizer\u003cbr\u003e __02.1.3 Creating a Custom Tokenizer\u003cbr\u003e __02.1.4 Using the Hugging Face Pre-trained Tokenizer\u003cbr\u003e _02.2 Datasets library\u003cbr\u003e __02.2.1 Using the Hugging Face Dataset\u003cbr\u003e __02.2.2 Using the Hugging Face Dataset in PyTorch\u003cbr\u003e _02.3 Model Fine Tuning\u003cbr\u003e __02.3.1 Preferences\u003cbr\u003e __02.3.2 Learning\u003cbr\u003e __02.3.3 Inference\u003cbr\u003e _02.4 Sharing the Hugging Face Model\u003cbr\u003e __02.4.1 Model Sharing\u003cbr\u003e __02.4.2 Using Spaces\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 3 PyTorch Transformer Model\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _03.1 PyTorch Transformer Components\u003cbr\u003e _03.2 Embedding\u003cbr\u003e __03.2.1 Implementing the embedding layer\u003cbr\u003e _03.3 Positional encoding\u003cbr\u003e _03.4 Masking \u003cbr\u003e_03.5 Transformer Encoder Components\u003cbr\u003e _03.6 Transformer Decoder Components\u003cbr\u003e _03.7 PyTorch Transformer Layer\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 4: Transfer Learning with PyTorch and HuggingFace\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _04.1 The Need for Transfer Learning\u003cbr\u003e _04.2 How to Use Transfer Learning\u003cbr\u003e _04.3 Pre-trained model repository\u003cbr\u003e _04.4 Pre-training model\u003cbr\u003e __04.4.1 Natural Language Processing (NLP)\u003cbr\u003e __04.4.2 Computer Vision\u003cbr\u003e __04.4.3 Voice Processing\u003cbr\u003e _04.5 Project 1: Creating a Classifier by Fine-Tuning the BERT-base-uncased Model\u003cbr\u003e __04.5.1 Custom Dataset Class\u003cbr\u003e __04.5.2 Creating a DataLoader\u003cbr\u003e __04.5.3 Inference\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 5 Large-Scale Language Models\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _05.1 Large-Scale Language Model (LLM)\u003cbr\u003e _05.2 Key factors determining performance\u003cbr\u003e __05.2.1 Network size: Number of encoder and decoder layers\u003cbr\u003e _05.3 Leading LLM\u003cbr\u003e __05.3.1 BERT and related models\u003cbr\u003e __05.3.2 GPT\u003cbr\u003e __05.3.3 BART\u003cbr\u003e _05.4 Creating a Custom LLM\u003cbr\u003e __05.4.1 Clincal-BERT Implementation\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 6: Transformer NLP Tasks\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _06.1 NLP Task\u003cbr\u003e _06.2 Text classification\u003cbr\u003e __06.2.1 Architecture suitable for text classification \u003cbr\u003e__06.2.2 Text classification using transformer fine-tuning\u003cbr\u003e __06.2.3 Long sequence processing\u003cbr\u003e __06.2.4 Document Chunking Implementation Example\u003cbr\u003e __06.2.5 Hierarchical Attention Implementation Example\u003cbr\u003e _06.3 Text Generation\u003cbr\u003e __06.3.1 Project 2: Generating Text That Sounds Like Shakespeare\u003cbr\u003e _06.4 Transformer Chatbot\u003cbr\u003e __06.4.1 Project 3: Clinic Question Answering (AI Doctor) Transformer\u003cbr\u003e _06.5 Learning with PEFT and LoRA\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 7 Computer Vision (CV) Models\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _07.1 Image Preprocessing\u003cbr\u003e __07.1.1 Image preprocessing example\u003cbr\u003e _07.2 Vision Transformer Architecture\u003cbr\u003e __07.2.1 Project 4: AI Ophthalmologist\u003cbr\u003e _07.3 Distillation Transformer\u003cbr\u003e __07.3.1 DeiT's pre-learning process\u003cbr\u003e __07.3.1 Advantages of DeiT\u003cbr\u003e _07.4 Detection Transformer\u003cbr\u003e __07.4.1 Project 5: Object Detection Model\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 8: Transformer Computer Vision Tasks\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _08.1 Computer Vision Tasks\u003cbr\u003e __08.1.1 Image Classification\u003cbr\u003e __08.1.2 Image Segmentation\u003cbr\u003e __08.1.3 Project 6: Image Segmentation for a Diet Calculator \u003cbr\u003e_08.2 Diffusion Model: Unconditional Image Generation\u003cbr\u003e __08.2.1 Forward Diffusion\u003cbr\u003e __08.2.2 Backward Diffusion\u003cbr\u003e __08.2.3 Inference Process\u003cbr\u003e __08.2.4 Learnable Parameters\u003cbr\u003e __08.2.5 Implementing the DogGenDiffuion Project\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 9 Voice Processing Model\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _09.1 Voice Processing\u003cbr\u003e __09.1.1 Voice preprocessing example\u003cbr\u003e _09.2 Whisper Model\u003cbr\u003e __09.2.1 Whisper_Nep model development process\u003cbr\u003e _09.3 Wav2Vec model\u003cbr\u003e __09.3.1 Wav2Vec application\u003cbr\u003e _09.4 Speech T5 Model\u003cbr\u003e __09.4.1 Input\/Output Representation\u003cbr\u003e __09.4.2 Cross-modal presentation\u003cbr\u003e __09.4.3 Encoder-Decoder Architecture\u003cbr\u003e __09.4.4 Pre-study\u003cbr\u003e __09.4.5 Fine Tuning and Applications\u003cbr\u003e _09.5 Comparison of Whisper, Wav2Vec 2.0, and SpeechT5\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 10: Transformer Voice Processing Tasks\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _10.1 Voice Processing Tasks\u003cbr\u003e __10.1.1 Speech to text\u003cbr\u003e __10.1.2 Project 7: Speech to Text Conversion Using Whisper\u003cbr\u003e _10.2 Text to Speech\u003cbr\u003e __10.2.1 Project 8: Text to Speech\u003cbr\u003e _10.3 Audio to Audio Conversion \u003cbr\u003e__10.3.1 Project 9: Improving Audio Quality with Noise Reduction\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 11: Transformers for Table Data Processing\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _11.1 Processing Table Data Using Transformers\u003cbr\u003e __11.1.1 TAPAS Architecture\u003cbr\u003e _11.2 TabTransformer Architecture\u003cbr\u003e _11.3 FT Transformer Architecture\u003cbr\u003e __11.3.1 Feature Tokenizer\u003cbr\u003e __11.3.2 Merging numeric and categorical features\u003cbr\u003e __11.3.3 Transformer\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 12: Transformers for Regression and Classification Tasks on Tabular Data\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _12.1 Transformer for classification work\u003cbr\u003e __12.1.1 dataset\u003cbr\u003e __12.1.2 Target Variables\u003cbr\u003e __12.1.3 Data Preprocessing\u003cbr\u003e __12.1.4 Settings\u003cbr\u003e __12.1.5 Training and Evaluation with Three Models\u003cbr\u003e __12.1.6 Evaluation Results\u003cbr\u003e __12.1.7 Analysis\u003cbr\u003e _12.2 Transformer for regression tasks\u003cbr\u003e __12.2.1 Dataset\u003cbr\u003e __12.2.2 Data Preprocessing\u003cbr\u003e __12.2.3 Settings\u003cbr\u003e __12.2.4 Learning and Evaluation\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 13 Multimodal Transformers\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _13.1 Multimodal Architecture\u003cbr\u003e __13.1.1 ImageBind\u003cbr\u003e __13.1.2 CLIP\u003cbr\u003e _13.2 Multimodal work\u003cbr\u003e __13.2.1 Feature Extraction\u003cbr\u003e __13.2.2 Text to Image\u003cbr\u003e __13.2.3 Image to Text \u003cbr\u003e__13.2.4 Visual Question Answering\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 14: Transformer Reinforcement Learning\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _14.1 Reinforcement Learning\u003cbr\u003e _14.2 PyTorch Techniques (Models) for Reinforcement Learning\u003cbr\u003e __14.2.1 Stable Baseline3\u003cbr\u003e __14.2.2 Gymnasium\u003cbr\u003e _14.3 How to Perform Reinforcement Learning\u003cbr\u003e _14.4 Transformers for Reinforcement Learning\u003cbr\u003e __14.4.1 Decision Transformer\u003cbr\u003e __14.4.2 Trajectory Transformer\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 15: Exporting, Serving, and Deploying Models\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _15.1 Project 10: Exporting and Serializing Models\u003cbr\u003e __15.1.1 Exporting and Importing PyTorch Models\u003cbr\u003e __15.1.2 Saving multiple models\u003cbr\u003e _15.2 Exporting models to ONNX format\u003cbr\u003e _15.3 Serving Models with FastAPI\u003cbr\u003e __15.3.1 Advantages of FastAPI\u003cbr\u003e __15.3.2 FastAPI Application for Model Serving\u003cbr\u003e __15.3.3 FastAPI for serving semantic segmentation models\u003cbr\u003e _15.4 Serving PyTorch Models on Mobile Devices\u003cbr\u003e _15.5 Deploying the HuggingFace Transformer Model on AWS\u003cbr\u003e __15.5.1 Deploying via Amazon SageMaker\u003cbr\u003e __15.5.2 Deploying via AWS Lambda and Amazon API Gateway\u003cbr\u003e \u003cbr\u003e\u003cb\u003eChapter 16: Transformer Model Interpretability and Visualization\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _16.1 Explainability vs. Interpretability Concepts\u003cbr\u003e __16.1.1 Interpretability\u003cbr\u003e __16.1.2 Explainability\u003cbr\u003e _16.2 Explainability and Interpretability Tools\u003cbr\u003e _16.3 CAPTUM for Transformer Prediction Analysis\u003cbr\u003e __16.3.1 Loading the model\u003cbr\u003e __16.3.2 Input Preparation\u003cbr\u003e __16.3.3 Layer Integral Gradient\u003cbr\u003e __16.3.4 Visualization\u003cbr\u003e _16.4 TensorBoard for PyTorch Models\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 17: Best Practices and Debugging PyTorch Models\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _17.1 Best Practices for Implementing Transformer Models\u003cbr\u003e __17.1.1 Using Hugging Face\u003cbr\u003e __17.1.2 General Considerations for PyTorch Models\u003cbr\u003e _17.2 PyTorch Debugging Techniques\u003cbr\u003e __17.2.1 Syntax error\u003cbr\u003e __17.2.2 Runtime Error\u003cbr\u003e __17.2.3 Logical Error\u003cbr\u003e __17.2.4 General Guidelines for Debugging PyTorch ML Models\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e \",\"\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate5150\/MidCate001\/514904739.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e \",\"\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003e★10 Projects Covered in This Book★\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 1. Create a classifier by fine-tuning the BERT-base-uncased model.\u003cbr\u003e 2. \u003cbr\u003eGenerate text that sounds like Shakespeare\u003cbr\u003e 3. Creating an AI Clinic Q\u0026amp;A Chatbot\u003cbr\u003e 4.\u003cbr\u003e Implementing AI doctors in ophthalmology\u003cbr\u003e 5.\u003cbr\u003e Creating a program for object detection\u003cbr\u003e 6.\u003cbr\u003e Implementing a diet calculator that classifies food photos\u003cbr\u003e 7.\u003cbr\u003e Voice to Text Conversion with Whisper\u003cbr\u003e 8.\u003cbr\u003e Text-to-speech conversion using SpeechT5\u003cbr\u003e 9.\u003cbr\u003e Improve audio quality with noise removal\u003cbr\u003e 10.\u003cbr\u003e Exporting and Serializing PyTorch Models\u003cbr\u003e\u003cbr\u003e \u003cb\u003e★10 Key Keywords Covered in This Book★\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 1.\u003cbr\u003e Transformer architecture\u003cbr\u003e 2.\u003cbr\u003e Hugging Face Ecosystem\u003cbr\u003e 3.\u003cbr\u003e PyTorch-based model implementation\u003cbr\u003e 4.\u003cbr\u003e transfer learning\u003cbr\u003e 5. LLM (Large-Scale Language Model)\u003cbr\u003e 6. NLP tasks (text classification and generation)\u003cbr\u003e 7.\u003cbr\u003e Computer Vision Transformer Model\u003cbr\u003e 8.\u003cbr\u003e Voice Processing Transformer\u003cbr\u003e 9.\u003cbr\u003e Multimodal transformer\u003cbr\u003e 10.\u003cbr\u003e Model serving and distribution\u003cbr\u003e\u003cbr\u003e \u003cb\u003e★This book's target audience★\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e * Data scientists who need full guidelines for the HuggingFace Transformer model and library. \u003cbr\u003e* AI engineers who are curious about practical projects in various fields such as natural language processing, computer vision, and speech recognition.\u003cbr\u003e * Project managers who need to understand AI and machine learning project management and implementation\u003cbr\u003e * Anyone who wants to know how transformer models are used in deep learning and machine learning!\u003cbr\u003e\u003cbr\u003e \u003cb\u003e★Amazon Readers' Recommendations★\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e This book systematically presents insightful, advanced techniques that leverage the powerful capabilities of PyTorch and HuggingFace.\u003cbr\u003e A suitable reference book for deep learning enthusiasts who want to delve deeper into the practical applications of transformers.\u003cbr\u003e The content is well-structured and thoroughly engaging for readers interested in cutting-edge machine learning technologies.\u003cbr\u003e - sunita\u003cbr\u003e\u003cbr\u003e This is a great resource if you want to start building Transformer-based models and develop projects related to NLP, vision, and audio. \u003cbr\u003eThis book provides complete code examples that you can run and practice with, and includes quizzes to help you test your knowledge.\u003cbr\u003e Overall, it's a very good book.\u003cbr\u003e - Ganesh\u003cbr\u003e\u003cbr\u003e The book begins by detailing the Transformer model architecture.\u003cbr\u003e The full journey covers several practical examples for implementing the model.\u003cbr\u003e Covers everything from creating basic custom tokenizers to computer vision, speech processing, and multimodal projects.\u003cbr\u003e All code examples are provided as ready-to-run Google Colab files, making it easy for anyone to follow along and build their own projects.\u003cbr\u003e If you're interested in learning more about Transformer models but feel overwhelmed by the sheer volume of content, this book is the perfect \"learn by doing\" guide, guiding you through a logical sequence and providing easy-to-follow code examples.\u003cbr\u003e - fabio santana\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e \"]\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS\u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n \u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e- \u003cstrong\u003eDate of issue:\u003c\/strong\u003e February 7, 2025\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 292 pages | 170*232*20mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9791193083277\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN10:\u003c\/strong\u003e 1193083273 \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default Title","offer_id":43893259010090,"sku":"138563","price":38.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/243c700e23b8fcc80ff78c8853e0fa93.jpg?v=1765394376","url":"https:\/\/librairie.coreenne.fr\/en\/products\/138563","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}