product
7445465Practical Deep Learning, 2nd Editionhttps://www.gandhi.com.mx/practical-deep-learning--2nd-edition-9781718504219/phttps://gandhi.vtexassets.com/arquivos/ids/7044091/image.jpg?v=6387977124499700007801084MXNNo Starch PressInStock/Ebooks/<p><strong>Deep learning made simple.</strong></p><p>Dip into deep learning without drowning in theory with this fully updated edition of <em>Practical Deep Learning</em> from experienced author and AI expert Ronald T. Kneusel.</p><p>After a brief review of basic math and coding principles, youll dive into hands-on experiments and learn to build working models for everything from image analysis to creative writing, and gain a thorough understanding of how each technique works under the hood. Whether youre a developer looking to add AI to your toolkit or a student seeking practical machine learning skills, this book will teach you:</p><ul><li>How neural networks work and how theyre trained</li><li>How to use classical machine learning models</li><li>How to develop a deep learning model from scratch</li><li>How to evaluate models with industry-standard metrics</li><li>How to create your own generative AI models</li></ul><p>Each chapter emphasizes practical skill development and experimentation, building to a case study that incorporates everything youve learned to classify audio recordings. Examples of working code you can easily run and modify are provided, and all code is freely available on GitHub. With <em>Practical Deep Learning</em>, second edition, youll gain the skills and confidence you need to build real AI systems that solve real problems.</p><p><strong>New to this edition:</strong> Material on computer vision, fine-tuning and transfer learning, localization, self-supervised learning, generative AI for novel image creation, and large language models for in-context learning, semantic search, and retrieval-augmented generation (RAG).</p>...7074140Practical Deep Learning, 2nd Edition7801084https://www.gandhi.com.mx/practical-deep-learning--2nd-edition-9781718504219/phttps://gandhi.vtexassets.com/arquivos/ids/7044091/image.jpg?v=638797712449970000InStockMXN99999DIEbook20259781718504219_W3siaWQiOiJkOTg2MjdkZi01OWNmLTRmOGMtYjNmOS0wOTQ4NmJlMTNjMDciLCJsaXN0UHJpY2UiOjEwODQsImRpc2NvdW50IjozMDQsInNlbGxpbmdQcmljZSI6NzgwLCJpbmNsdWRlc1RheCI6dHJ1ZSwicHJpY2VUeXBlIjoiV2hvbGVzYWxlIiwiY3VycmVuY3kiOiJNWE4iLCJmcm9tIjoiMjAyNS0wNy0wOFQwMDowMDowMFoiLCJyZWdpb24iOiJNWCIsImlzUHJlb3JkZXIiOmZhbHNlfV0=9781718504219_<p><strong><em>Practical Deep Learning, 2nd Edition</em> is your gateway into AI, equipping you with the knowledge and confidence to build powerful AI models using the latest architectures and techniques.</strong></p><p>If youve been curious about artificial intelligence and machine learning but didnt know where to start, this is the book youve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, <em>Practical Deep Learning, 2nd Edition</em> teaches you the why of deep learning and will inspire you to explore further.</p><p>All you need is basic familiarity with computer programming and high school maththe book will cover the rest. After an introduction to Python, youll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models performance.</p><p>Youll also learn:</p><ul><li>How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines</li><li>How neural networks work and how theyre trained</li><li>How to use convolutional neural networks</li><li>How to develop a successful deep learning model from scratch</li></ul><p>Youll conduct experiments along the way, building to a final case study that incorporates everything youve learned.</p><p>This second edition is thoroughly revised and updated, and adds six new chapters to further your exploration of deep learning from basic CNNs to more advanced models. New chapters cover fine tuning, transfer learning, object detection, semantic segmentation, multilabel classification, self-supervised learning, generative adversarial networks, and large language models.</p><p>The perfect introduction to this dynamic, ever-expanding field, <em>Practical Deep Learning, 2nd Edition</em> will give you the skills and confidence to dive into your own machine learning projects.</p>...(*_*)9781718504219_<p><strong>Deep learning made simple.</strong></p><p>Dip into deep learning without drowning in theory with this fully updated edition of <em>Practical Deep Learning</em> from experienced author and AI expert Ronald T. Kneusel.</p><p>After a brief review of basic math and coding principles, youll dive into hands-on experiments and learn to build working models for everything from image analysis to creative writing, and gain a thorough understanding of how each technique works under the hood. Whether youre a developer looking to add AI to your toolkit or a student seeking practical machine learning skills, this book will teach you:</p><ul><li>How neural networks work and how theyre trained</li><li>How to use classical machine learning models</li><li>How to develop a deep learning model from scratch</li><li>How to evaluate models with industry-standard metrics</li><li>How to create your own generative AI models</li></ul><p>Each chapter emphasizes practical skill development and experimentation, building to a case study that incorporates everything youve learned to classify audio recordings. Examples of working code you can easily run and modify are provided, and all code is freely available on GitHub. With <em>Practical Deep Learning</em>, second edition, youll gain the skills and confidence you need to build real AI systems that solve real problems.</p><p><strong>New to this edition:</strong> Material on computer vision, fine-tuning and transfer learning, localization, self-supervised learning, generative AI for novel image creation, and large language models for in-context learning, semantic search, and retrieval-augmented generation (RAG).</p>...9781718504219_No Starch Presslibro_electonico_9781718504219_9781718504219Ronald T.InglésMéxico2025-07-08T00:00:00+00:00https://getbook.kobo.com/koboid-prod-public/randomhousewh-epub-8c0d80a1-5227-4f83-932c-1528da8d9c61.epub2025-07-08T00:00:00+00:00No Starch Press