{"product_id":"inside-deep-learning-math-algorithms-models-9781617298639","title":"Inside Deep Learning: Math, Algorithms, Models","description":"\u003cb\u003eJourney through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems.\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003eIn \u003ci\u003eInside Deep Learning\u003c\/i\u003e, you will learn how to: \u003cp\u003e\u003c\/p\u003e Implement deep learning with PyTorch\u003cbr\u003e Select the right deep learning components\u003cbr\u003e Train and evaluate a deep learning model\u003cbr\u003e Fine tune deep learning models to maximize performance\u003cbr\u003e Understand deep learning terminology\u003cbr\u003e Adapt existing PyTorch code to solve new problems \u003cp\u003e\u003c\/p\u003e \u003ci\u003eInside Deep Learning\u003c\/i\u003e is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped--you'll dive into math, theory, and practical applications. Everything is clearly explained in plain English. \u003cp\u003e\u003c\/p\u003e Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. \u003cp\u003e\u003c\/p\u003e About the technology\u003cbr\u003e Deep learning doesn't have to be a black box! Knowing how your models and algorithms actually work gives you greater control over your results. And you don't have to be a mathematics expert or a senior data scientist to grasp what's going on inside a deep learning system. This book gives you the practical insight you need to understand and explain your work with confidence. \u003cp\u003e\u003c\/p\u003e About the book\u003cbr\u003e \u003ci\u003eInside Deep Learning\u003c\/i\u003e illuminates the inner workings of deep learning algorithms in a way that even machine learning novices can understand. You'll explore deep learning concepts and tools through plain language explanations, annotated code, and dozens of instantly useful PyTorch examples. Each type of neural network is clearly presented without complex math, and every solution in this book can run using readily available GPU hardware! \u003cp\u003e\u003c\/p\u003e What's inside \u003cp\u003e\u003c\/p\u003e Select the right deep learning components\u003cbr\u003e Train and evaluate a deep learning model\u003cbr\u003e Fine tune deep learning models to maximize performance\u003cbr\u003e Understand deep learning terminology \u003cp\u003e\u003c\/p\u003eAbout the reader\u003cbr\u003e For Python programmers with basic machine learning skills. \u003cp\u003e\u003c\/p\u003e About the author\u003cbr\u003e \u003cb\u003eEdward Raff\u003c\/b\u003e is a Chief Scientist at Booz Allen Hamilton, and the author of the JSAT machine learning library. \u003cp\u003e\u003c\/p\u003eTable of Contents\u003cbr\u003e PART 1 FOUNDATIONAL METHODS\u003cbr\u003e 1 The mechanics of learning\u003cbr\u003e 2 Fully connected networks\u003cbr\u003e 3 Convolutional neural networks\u003cbr\u003e 4 Recurrent neural networks\u003cbr\u003e 5 Modern training techniques\u003cbr\u003e 6 Common design building blocks\u003cbr\u003e PART 2 BUILDING ADVANCED NETWORKS\u003cbr\u003e 7 Autoencoding and self-supervision\u003cbr\u003e 8 Object detection\u003cbr\u003e 9 Generative adversarial networks\u003cbr\u003e 10 Attention mechanisms\u003cbr\u003e 11 Sequence-to-sequence\u003cbr\u003e 12 Network design alternatives to RNNs\u003cbr\u003e 13 Transfer learning\u003cbr\u003e 14 Advanced building blocks\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eEdward Raff \u003c\/b\u003eis a Chief Scientist at Booz Allen Hamilton, and the author of the JSAT machine learning library. His research includes deep learning, malware detection, reproducibility in ML, fairness\/bias, and high performance computing. He is also a visiting professor at the University of Maryland, Baltimore County and teaches deep learning in the Data Science department. Dr Raff has over 40 peer reviewed publications, three best paper awards, and has presented at numerous major conferences.\u003cbr\u003e","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":50495453987090,"sku":"9781617298639","price":55.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_56abcd8b-d46d-4a63-b01e-5b434724fa4b.jpg?v=1730684973","url":"https:\/\/surprise-castle.myshopify.com\/products\/inside-deep-learning-math-algorithms-models-9781617298639","provider":"Surprise Castle","version":"1.0","type":"link"}