{"product_id":"9781838823412-deep-learning-wtensorflow-2","title":"Deep Learning with TensorFlow 2 and Keras","description":"\u003cmeta content=\"text\/html; charset=utf-8\" http-equiv=\"Content-Type\"\u003e\u003cp\u003e\u003cspan\u003eRegression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition. Deep Learning with TensorFlow 2 and Keras, Second Edition teaches deep learning techniques alongside TensorFlow (TF) and Keras. The book introduces neural networks with TensorFlow, runs through the main applications, covers two working example apps, and then dives into TF and cloudin production, TF mobile, and using TensorFlow with AutoML. \u003cp\u003e\u003cb\u003eBuild machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices\u003c\/b\u003e\u003c\/p\u003eKey Features\u003cul\u003e\n\u003cli\u003eIntroduces and then uses TensorFlow 2 and Keras right from the start\u003c\/li\u003e\n\u003cli\u003eTeaches key machine and deep learning techniques\u003c\/li\u003e\n\u003cli\u003eUnderstand the fundamentals of deep learning and machine learning through clear explanations and extensive code samples\u003c\/li\u003e\n\u003c\/ul\u003eBook Description\u003cp\u003eDeep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available.\u003c\/p\u003e\n\u003cp\u003eTensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before.\u003c\/p\u003e\n\u003cp\u003eThis book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.\u003c\/p\u003eWhat you will learn\u003cul\u003e\n\u003cli\u003eBuild machine learning and deep learning systems with TensorFlow 2 and the Keras API\u003c\/li\u003e\n\u003cli\u003eUse Regression analysis, the most popular approach to machine learning\u003c\/li\u003e\n\u003cli\u003eUnderstand ConvNets (convolutional neural networks) and how they are essential for deep learning systems such as image classifiers\u003c\/li\u003e\n\u003cli\u003eUse GANs (generative adversarial networks) to create new data that fits with existing patterns\u003c\/li\u003e\n\u003cli\u003eDiscover RNNs (recurrent neural networks) that can process sequences of input intelligently, using one part of a sequence to correctly interpret another\u003c\/li\u003e\n\u003cli\u003eApply deep learning to natural human language and interpret natural language texts to produce an appropriate response\u003c\/li\u003e\n\u003cli\u003eTrain your models on the cloud and put TF to work in real environments\u003c\/li\u003e\n\u003cli\u003eExplore how Google tools can automate simple ML workflows without the need for complex modeling\u003c\/li\u003e\n\u003c\/ul\u003eWho this book is for\u003cp\u003eThis book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice required to use Keras, TensorFlow 2, and AutoML to build machine learning systems. Some knowledge of machine learning is expected.\u003c\/p\u003e\n\u003cbr\u003e\u003c\/span\u003e\u003c\/p\u003e","brand":"Rarewaves","offers":[{"title":"Default Title","offer_id":41078921166945,"sku":"9781838823412","price":45.22,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0092\/7504\/8033\/files\/7625a5e1a161cf3a9259e4d3ae27547d.png?v=1700717439","url":"https:\/\/www.rarewaves.com\/products\/9781838823412-deep-learning-wtensorflow-2","provider":"Rarewaves.com","version":"1.0","type":"link"}