{"product_id":"deep-learning-with-pytorch-book-by-eli-stevens-luca-antiga-and-thomas-viehmann","title":"Deep Learning with PyTorch\nBook by Eli Stevens, Luca Antiga, and Thomas Viehmann","description":"\u003cp\u003e\u003cspan\u003ePyTorch core developer Howard Huang updates the bestselling original Deep Learning with PyTorch with new insights into the transformers architecture and generative AI models.\u003cbr\u003e\u003cbr\u003eInstantly familiar to anyone who knows PyData tools like NumPy, PyTorch simplifies deep learning without sacrificing advanced features. In this book you’ll learn how to create your own neural network and deep learning systems and take full advantage of PyTorch’s built-in tools for automatic differentiation, hardware acceleration, distributed training, and more. You’ll discover how easy PyTorch makes it to build your entire DL pipeline, including using the PyTorch Tensor API, loading data in Python, monitoring training, and visualizing results. Each new technique you learn is put into action with practical code examples in each chapter, culminating into you building your own convolution neural networks, transformers, and even a real-world medical image classifier.\u003cbr\u003e\u003cbr\u003eIn \u003c\/span\u003e\u003cspan class=\"a-text-italic\"\u003eDeep Learning with PyTorch, Second Edition\u003c\/span\u003e\u003cspan\u003e you’ll find:\u003cbr\u003e\u003cbr\u003e• Deep learning fundamentals reinforced with hands-on projects\u003cbr\u003e• Mastering PyTorch's flexible APIs for neural network development\u003cbr\u003e• Implementing CNNs, transformers, and diffusion models\u003cbr\u003e• Optimizing models for training and deployment\u003cbr\u003e• Generative AI models to create images and text\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eAbout the technology\u003c\/span\u003e\u003cspan\u003e\u003cbr\u003e\u003cbr\u003eThe powerful PyTorch library makes deep learning simple—without sacrificing the features you need to create efficient neural networks, LLMs, and other ML models. Pythonic by design, it’s instantly familiar to users of NumPy, Scikit-learn, and other ML frameworks. This thoroughly-revised second edition covers the latest PyTorch innovations, including how to create and refine generative AI models.\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eAbout the book\u003c\/span\u003e\u003cspan\u003e\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"a-text-italic\"\u003eDeep Learning with PyTorch, Second Edition\u003c\/span\u003e\u003cspan\u003e shows you how to build neural network models using the latest version of PyTorch. Clear explanations and practical projects help you master the fundamentals and explore advanced architectures including transformers and LLMs. Along the way you’ll learn techniques for training using augmented data, improving model architecture, and fine tuning.\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eWhat's inside\u003c\/span\u003e\u003cspan\u003e\u003cbr\u003e\u003cbr\u003e• PyTorch APIs for neural network development\u003cbr\u003e• LLMs, transformers, and diffusion models\u003cbr\u003e• Model training and deployment\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eAbout the reader\u003c\/span\u003e\u003cspan\u003e\u003cbr\u003e\u003cbr\u003eFor Python programmers with a background in machine learning.\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eAbout the author\u003c\/span\u003e\u003cspan\u003e\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eHoward Huang\u003c\/span\u003e\u003cspan\u003e is a software engineer and developer on the PyTorch library focusing on large scale, distributed training. \u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eEli Stevens\u003c\/span\u003e\u003cspan\u003e, \u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eLuca Antiga\u003c\/span\u003e\u003cspan\u003e, and \u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eThomas Viehmann\u003c\/span\u003e\u003cspan\u003e authored the first edition of \u003c\/span\u003e\u003cspan class=\"a-text-italic\"\u003eDeep Learning with PyTorch\u003c\/span\u003e\u003cspan\u003e.\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eTable of Contents\u003c\/span\u003e\u003cspan\u003e\u003cbr\u003e\u003cbr\u003ePart 1\u003cbr\u003e1 Introducing deep learning and the PyTorch library\u003cbr\u003e2 Pretrained networks\u003cbr\u003e3 It starts with a tensor\u003cbr\u003e4 Real-world data representation using tensors\u003cbr\u003e5 The mechanics of learning\u003cbr\u003e6 Using a neural network to fit the data\u003cbr\u003e7 Telling birds from airplanes: Learning from images\u003cbr\u003e8 Using convolutions to generalize\u003cbr\u003ePart 2\u003cbr\u003e9 How transformers work\u003cbr\u003e10 Diffusion models for images\u003cbr\u003e11 Using PyTorch to fight cancer\u003cbr\u003e12 Combining data sources into a unified dataset\u003cbr\u003e13 Training a classification model to detect suspected tumors\u003cbr\u003e14 Improving training with metrics and augmentation\u003cbr\u003e15 Using segmentation to find suspected nodules\u003cbr\u003e16 Training models on multiple GPU\u003cbr\u003e17 Deploying to production\u003c\/span\u003e\u003c\/p\u003e","brand":"Book Lovers BD","offers":[{"title":"Paperback","offer_id":44577504231466,"sku":null,"price":560.0,"currency_code":"BDT","in_stock":true},{"title":"Hardcover","offer_id":44577504264234,"sku":null,"price":690.0,"currency_code":"BDT","in_stock":true},{"title":"eBook","offer_id":44577504297002,"sku":null,"price":150.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0725\/5126\/8394\/files\/rn-image_picker_lib_temp_9ef34933-d47b-4f80-9cf2-95207e7dd544.jpg?v=1784055440","url":"https:\/\/bookycafebd.shop\/products\/deep-learning-with-pytorch-book-by-eli-stevens-luca-antiga-and-thomas-viehmann","provider":"BookYcafe","version":"1.0","type":"link"}