{"product_id":"practical-deep-learning-isbn-9781718500747","title":"Practical Deep Learning","description":"\u003cb\u003e\u003ci\u003ePractical Deep Learning\u003c\/i\u003e teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eIf you’ve been curious about artificial intelligence and machine learning but didn’t know where to start, this is the book you’ve been waiting for. Focusing on the subfield of machine learning known as \u003ci\u003edeep learning\u003c\/i\u003e, 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, \u003ci\u003ePractical Deep Learning\u003c\/i\u003e teaches you the why of deep learning and will inspire you to explore further.\u003cbr\u003e\u003cbr\u003eAll you need is basic familiarity with computer programming and high school math—the book will cover the rest. After an introduction to Python, you’ll 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.\u003cbr\u003e\u003cbr\u003eYou’ll also learn:\u003cbr\u003e\u003cul\u003e\u003cli\u003eHow to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines\u003c\/li\u003e\u003c\/ul\u003e\u003cul\u003e\u003cli\u003eHow neural networks work and how they’re trained\u003c\/li\u003e\u003c\/ul\u003e\u003cul\u003e\u003cli\u003eHow to use convolutional neural networks\u003c\/li\u003e\u003c\/ul\u003e\u003cul\u003e\u003cli\u003eHow to develop a successful deep learning model from scratch\u003c\/li\u003e\u003c\/ul\u003e You’ll conduct experiments along the way, building to a final case study that incorporates everything you’ve learned. \u003cbr\u003e\u003cbr\u003eThe perfect introduction to this dynamic, ever-expanding field, \u003ci\u003ePractical Deep Learning\u003c\/i\u003e will give you the skills and confidence to dive into your own machine learning projects.\u003cb\u003eForeword \u003c\/b\u003eby Michael C. Mozer, PhD\u003cbr\u003e\u003cb\u003eAcknowledgments\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eIntroduction\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eChapter 1: \u003c\/b\u003eGetting Started\u003cbr\u003e\u003cb\u003eChapter 2: \u003c\/b\u003eUsing Python\u003cbr\u003e\u003cb\u003eChapter 3: \u003c\/b\u003eUsing NumPy\u003cbr\u003e\u003cb\u003eChapter 4:\u003c\/b\u003e Working With Data\u003cbr\u003e\u003cb\u003eChapter 5: \u003c\/b\u003eBuilding Datasets\u003cbr\u003e\u003cb\u003eChapter 6: \u003c\/b\u003eClassical Machine Learning\u003cbr\u003e\u003cb\u003eChapter 7: \u003c\/b\u003eExperiments with Classical Models\u003cbr\u003e\u003cb\u003eChapter 8:\u003c\/b\u003e Introduction to Neural Networks\u003cbr\u003e\u003cb\u003eChapter 9: \u003c\/b\u003eTraining A Neural Network\u003cbr\u003e\u003cb\u003eChapter 10:\u003c\/b\u003e Experiments with Neural Networks\u003cbr\u003e\u003cb\u003eChapter 11:\u003c\/b\u003e Evaluating Models\u003cbr\u003e\u003cb\u003eChapter 12: \u003c\/b\u003eIntroduction to Convolutional Neural Networks\u003cbr\u003e\u003cb\u003eChapter 13:\u003c\/b\u003e Experiments with Keras and MNIST\u003cbr\u003e\u003cb\u003eChapter 14: \u003c\/b\u003eExperiments with CIFAR-10\u003cbr\u003e\u003cb\u003eChapter 15: \u003c\/b\u003eA Case Study: Classifying Audio Samples\u003cbr\u003e\u003cb\u003eChapter 16: \u003c\/b\u003eGoing Further\u003cbr\u003e\u003cb\u003eIndex\u003c\/b\u003e\"\u003ci\u003ePractical Deep Learning with Python\u003c\/i\u003e is the perfect book for someone looking to break into deep learning. This book achieves an ideal balance between explaining prerequisite introductory material and exploring nuanced subtleties of the methods described. The reader will come away with a solid foundational understanding of the content as well as the practical knowledge required to apply the methods to real-world problems. Deep learning will continue to enable many breakthroughs in artificial intelligence applications and this book covers all that is needed to springboard into this exciting field.\"\u003cbr\u003e\u003cb\u003e—Matt Wilder, longtime neural network practitioner and owner of Wilder AI, a deep learning consulting company\u003cbr\u003e\u003cbr\u003e\u003c\/b\u003e\"Kneusel’s book tackles machine learning (classification) fantastically, helping anyone with an interest to learn and turning that interest into a skillset for future machine learning projects.\"\u003cb\u003e\u003cbr\u003e–GeekDude, GeekTechStuff\u003c\/b\u003eRon Kneusel has been working in the machine learning industry since 2003 and has been programming in Python since 2004. He received a PhD in Computer Science from UC Boulder in 2016 and is the author of two previous books: \u003ci\u003eNumbers and Computers\u003c\/i\u003e and \u003ci\u003eRandom Numbers and Computers\u003c\/i\u003e.","brand":"No Starch Press","offers":[{"title":"Default Title","offer_id":46303713525989,"sku":"NP9781718500747","price":59.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781718500747.jpg?v=1767735031","url":"https:\/\/k12savings.com\/es\/products\/practical-deep-learning-isbn-9781718500747","provider":"K12savings","version":"1.0","type":"link"}