{"product_id":"139080","title":"Natural Language Processing and Computer Vision Deep Learning Using PyTorch Transformers ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jXPBpXy9nzOVPs9V521LsJBQ7g3tZ.png?v=1765072679\" 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 Natural Language Processing and Computer Vision Deep Learning Using PyTorch Transformers \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\/122753048\/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\n\u003cdiv\u003e\u003cdiv\u003e Transformers are a model with excellent performance in the field of deep learning and are a core technology in the field of modern artificial intelligence. \u003cbr\u003eMastering Transformer and Vision Transformer technologies will give you differentiated capabilities and enable you to solve complex problems.\u003cbr\u003e This book covers a variety of information needed to build deep learning projects (services), from basic practice to deployment, in the fields of natural language processing and computer vision.\u003cbr\u003e Additionally, we will understand and practice models including transformer and vision transformer theory.\u003cbr\u003e This book provides comprehensive coverage of the latest trends in natural language processing and computer vision, as well as deep learning, and is recommended for those seeking to build powerful models through practical examples.\u003cbr\u003e\n\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents.\u003cbr\u003e \u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003ePart 1: Getting Started with PyTorch\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 1: Artificial Intelligence and Methodology\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e What is artificial intelligence?\u003cbr\u003e __History of Artificial Intelligence\u003cbr\u003e __Areas of AI Application\u003cbr\u003e machine learning system\u003cbr\u003e __Supervised learning\u003cbr\u003e __Unsupervised learning\u003cbr\u003e __Semi-supervised learning  \u003cbr\u003e__Reinforcement learning\u003cbr\u003e Machine Learning Architecture\u003cbr\u003e __Data preparation\u003cbr\u003e __modeling\u003cbr\u003e __Model Evaluation\u003cbr\u003e __Model deployment\u003cbr\u003e MLOps\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 2: Installing PyTorch\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e What is PyTorch?\u003cbr\u003e PyTorch Features\u003cbr\u003e Installing PyTorch\u003cbr\u003e __PyTorch CPU Installation\u003cbr\u003e __PyTorch GPU Installation\u003cbr\u003e __Google Colaboratory\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 3: PyTorch Basics\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e tensor\u003cbr\u003e __Create a tensor\u003cbr\u003e __Tensor properties\u003cbr\u003e __dimensional transformation\u003cbr\u003e __Data type setting\u003cbr\u003e __Device Settings\u003cbr\u003e __Device conversion\u003cbr\u003e __Tensor conversion of NumPy arrays\u003cbr\u003e __Tensor to NumPy array conversion\u003cbr\u003e Hypothesis\u003cbr\u003e __Hypothesis in Machine Learning\u003cbr\u003e __Statistical hypothesis testing examples\u003cbr\u003e loss function\u003cbr\u003e __square error\u003cbr\u003e __sum of squared errors\u003cbr\u003e __mean square error\u003cbr\u003e __Cross entropy\u003cbr\u003e Optimization\u003cbr\u003e __Gradient descent\u003cbr\u003e __learning rate\u003cbr\u003e __Optimization problem\u003cbr\u003e __Simple Linear Regression: NumPy\u003cbr\u003e Simple Linear Regression: PyTorch\u003cbr\u003e Datasets and Dataloaders\u003cbr\u003e __dataset\u003cbr\u003e __Data Loader\u003cbr\u003e __Multiple linear regression\u003cbr\u003e Model\/Dataset Separation\u003cbr\u003e __Module class\u003cbr\u003e __Nonlinear regression\u003cbr\u003e __Model Evaluation\u003cbr\u003e __Dataset separation\u003cbr\u003e Saving and Loading Models\u003cbr\u003e __Save\/Load Entire Model \u003cbr\u003e__Save\/Load Model State\u003cbr\u003e __Save\/Load Checkpoint\u003cbr\u003e Activation function\u003cbr\u003e __binary classification\u003cbr\u003e __sigmoid function\u003cbr\u003e __Binary cross entropy\u003cbr\u003e Binary Classification: PyTorch\u003cbr\u003e __Nonlinear activation function\u003cbr\u003e Forward propagation and backpropagation\u003cbr\u003e __Forward propagation calculation\u003cbr\u003e __Error calculation\u003cbr\u003e __Backpropagation calculation\u003cbr\u003e __Compare update results\u003cbr\u003e perceptron\u003cbr\u003e __single-layer perceptron\u003cbr\u003e __Multilayer Perceptron\u003cbr\u003e __Perceptron Model Practice\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 4: Advanced PyTorch\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Overfitting and underfitting\u003cbr\u003e __Solving overfitting and underfitting problems\u003cbr\u003e Batch normalization\u003cbr\u003e __Normalization type\u003cbr\u003e __Batch Normalization Solution\u003cbr\u003e Weight initialization\u003cbr\u003e __Constant initialization\u003cbr\u003e __Random initialization\u003cbr\u003e __Javier \u0026amp; Glorot Initialization\u003cbr\u003e __Kaiming \u0026amp; Heo Reset\u003cbr\u003e __Orthogonal initialization\u003cbr\u003e __Weight Initialization Practice\u003cbr\u003e Regularization\u003cbr\u003e __L1 regularization\u003cbr\u003e __L2 regularization\u003cbr\u003e __weight decay\u003cbr\u003e __Momentum\u003cbr\u003e __Elastic Net\u003cbr\u003e __Dropout\u003cbr\u003e __Gradient Clipping\u003cbr\u003e Data Augmentation and Transformation\u003cbr\u003e __text data\u003cbr\u003e __image data\u003cbr\u003e Pre-trained model\u003cbr\u003e __backbone\u003cbr\u003e __Transfer learning\u003cbr\u003e __Feature extraction and fine-tuning\u003cbr\u003e \u003cbr\u003e\u003cb\u003ePart 2 Natural Language Processing\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 5: Tokenization\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Word and character tokenization\u003cbr\u003e __word tokenization\u003cbr\u003e __Character tokenization\u003cbr\u003e Morpheme tokenization\u003cbr\u003e __Morpheme Dictionary\u003cbr\u003e __KoNLPy\u003cbr\u003e __NLTK\u003cbr\u003e __spaCy\u003cbr\u003e Subword tokenization\u003cbr\u003e __byte pair encoding\u003cbr\u003e __Wordpiece\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 6: Embedding\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e language model\u003cbr\u003e __Autoregressive language model\u003cbr\u003e __Statistical language model\u003cbr\u003e N-gram\u003cbr\u003e TF-IDF\u003cbr\u003e __word frequency\u003cbr\u003e __Document Frequency\u003cbr\u003e __Reverse document frequency\u003cbr\u003e __TF-IDF\u003cbr\u003e Word2Vec\u003cbr\u003e __Word vectorization\u003cbr\u003e __CBoW\u003cbr\u003e __Skip-gram\u003cbr\u003e __Hierarchical Softmax\u003cbr\u003e __negative sampling\u003cbr\u003e __Model Practice: Skip-gram\u003cbr\u003e __Model Practice: Gensim\u003cbr\u003e fastText\u003cbr\u003e __Model Practice\u003cbr\u003e recurrent neural networks\u003cbr\u003e __Recurrent Neural Network\u003cbr\u003e __long and short-term memory\u003cbr\u003e __Model Practice\u003cbr\u003e convolutional neural network\u003cbr\u003e __Convolution layer\u003cbr\u003e __Activation Map\u003cbr\u003e __Pooling\u003cbr\u003e __Fully connected layer\u003cbr\u003e __Model Practice\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 7: Transformers\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Transformer\u003cbr\u003e __Input embedding and positional encoding\u003cbr\u003e __Special Token\u003cbr\u003e __Transformer Encoder\u003cbr\u003e __Transformer Decoder\u003cbr\u003e __Model Practice\u003cbr\u003e GPT\u003cbr\u003e __GPT-1\u003cbr\u003e __GPT-2\u003cbr\u003e __GPT-3\u003cbr\u003e __GPT 3.5\u003cbr\u003e __GPT-4\u003cbr\u003e __Model Practice\u003cbr\u003e BERT\u003cbr\u003e __Pre-learning method\u003cbr\u003e __Model Practice\u003cbr\u003e BART\u003cbr\u003e __Pre-learning method \u003cbr\u003e__How to fine-tune\u003cbr\u003e __Model Practice\u003cbr\u003e ELECTRA\u003cbr\u003e __Pre-learning method\u003cbr\u003e __Model Practice\u003cbr\u003e T5\u003cbr\u003e __Model Practice\u003cbr\u003e\u003cbr\u003e \u003cb\u003ePart 3 Computer Vision\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 8: Image Classification\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e AlexNet\u003cbr\u003e __LeNet-5 and AlexNet\u003cbr\u003e __Model Training\u003cbr\u003e __Model Inference\u003cbr\u003e VGG\u003cbr\u003e __AlexNet and VGG-16\u003cbr\u003e __Model structure and data visualization\u003cbr\u003e __Fine-tuning and model training\u003cbr\u003e ResNet\u003cbr\u003e __ResNet Features\u003cbr\u003e __Model Implementation\u003cbr\u003e Grad-CAM\u003cbr\u003e __Class Activation Map\u003cbr\u003e __Grad-CAM\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 9: Object Detection\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Faster R-CNN\u003cbr\u003e __R-CNN\u003cbr\u003e __Fast R-CNN\u003cbr\u003e __Faster R-CNN\u003cbr\u003e __Model learning process\u003cbr\u003e __Model Practice\u003cbr\u003e SSD\u003cbr\u003e __Multi-scale feature maps\u003cbr\u003e __Basic box\u003cbr\u003e __Model learning process\u003cbr\u003e __Model Practice\u003cbr\u003e FCN\u003cbr\u003e __Upsampling\u003cbr\u003e __model structure\u003cbr\u003e __Model Practice\u003cbr\u003e Mask R-CNN\u003cbr\u003e __Features of Pyramid Network\u003cbr\u003e __Sort areas of interest\u003cbr\u003e __Mask Classifier\u003cbr\u003e __Model Practice\u003cbr\u003e YOLO\u003cbr\u003e __YOLOv1\u003cbr\u003e __YOLOv2\u003cbr\u003e __YOLOv3\u003cbr\u003e __YOLOv4 \/ YOLOv5\u003cbr\u003e __YOLOv6 \/ YOLOv7\u003cbr\u003e __Model Practice: YOLOv8\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 10: Vision Transformer\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ViT\u003cbr\u003e Comparison of Convolutional and ViT Models\u003cbr\u003e __ViT's inductive bias\u003cbr\u003e __ViT model\u003cbr\u003e __Patch Embedding\u003cbr\u003e __Encoder layer\u003cbr\u003e __Model Practice\u003cbr\u003e Swin Transformer \u003cbr\u003e__Difference between ViT and Swin transformer\u003cbr\u003e __Swin Transformer Model Structure\u003cbr\u003e __Model Practice\u003cbr\u003e CvT\u003cbr\u003e __Convolutional token embedding\u003cbr\u003e Convolutional embedding for attention\u003cbr\u003e __Model Practice\u003cbr\u003e\u003cbr\u003e \u003cb\u003ePart 4 Service Modeling\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 11: Deploying the Model\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Model lightweighting\u003cbr\u003e __quantization\u003cbr\u003e __Knowledge Distillation\u003cbr\u003e __Tensor decomposition\u003cbr\u003e __ONNX\u003cbr\u003e Model serving\u003cbr\u003e __Model Serving Web Framework\u003cbr\u003e __Postman\u003cbr\u003e Docker deployment\u003cbr\u003e __What is Docker?\u003cbr\u003e __Build and Deploy\u003cbr\u003e Demo application\u003cbr\u003e __Streamlet\u003cbr\u003e __Application distribution\u003cbr\u003e __PyTorch model integration\u003cbr\u003e\u003cbr\u003e Appendix A: PyTorch Lightning\u003cbr\u003e __Model Training\u003cbr\u003e __Trainer Class\u003cbr\u003e Appendix B: Hugging Face\u003cbr\u003e __PreTrainedConfig class\u003cbr\u003e __PreTrainedModel class\u003cbr\u003e __PreTrainedTokenizer class\u003cbr\u003e __PreTrainedFeatureExtractor class\u003cbr\u003e __PreTrainedImageProcessor class\u003cbr\u003e __Auto class\u003cbr\u003e __Trainer Class\u003cbr\u003e Appendix C: PyTorch Image Model\u003cbr\u003e __Create model\u003cbr\u003e __Pre-trained model\u003cbr\u003e __Fine tuning\u003cbr\u003e Appendix D: PyTorch Compiler\u003cbr\u003e Appendix E: Out of Memory\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\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\/TopCate4308\/MidCate009\/430780998(1).jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e  \u003cdiv\u003e\n\u003cb\u003e★ What this book covers ★\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ◎ Understanding machine learning and deep learning and in-depth practice using PyTorch\u003cbr\u003e ◎ Understanding tokenization of text data and practicing text embedding\u003cbr\u003e ◎ Understanding the Transformer model using self-attention and practicing the Transformer-based language model\u003cbr\u003e ◎ Understanding image classification models and verifying and visualizing their internal operating principles\u003cbr\u003e ◎ Understanding object detection models such as bounding box detection, semantic segmentation, and object segmentation\u003cbr\u003e ◎ Understand the feature pyramid network and region of interest alignment algorithm\u003cbr\u003e ◎ Understanding and practicing the vision transformer model that applies the transformer structure to the field of computer vision.\u003cbr\u003e ◎ Online API serving using web frameworks and Docker\u003cbr\u003e ◎ Building a deep learning demo application through a web application\u003cbr\u003e ◎ Practice with various management tools and techniques, including lightweighting, PyTorch Lightning, Hugging Face, and PyTorch Compiler. \u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\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 October 19, 2023\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 804 pages | 188*240*33mm\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 9791158394400 \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":43893339389994,"sku":"139080","price":54.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/b9a5bda6b94dd8e36f7057a092b9dba7.jpg?v=1765397200","url":"https:\/\/librairie.coreenne.fr\/en\/products\/139080","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}