{"product_id":"advanced-deep-learning-with-keras-apply-deep-learning-techniques-autoencoders-gans-variational-autoencoders-deep-reinforcement-learning-policy-g-paperback","title":"Advanced Deep Learning with Keras: Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy g - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eRowel Atienza\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003ePublisher's Note: This edition from 2018 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries. A new second edition, updated for 2020 and featuring TensorFlow 2 and coverage of unsupervised learning using mutual information, object detection, and semantic segmentation, has now been published.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eA comprehensive guide to advanced deep learning techniques, including autoencoders, GANs, VAEs, and deep reinforcement learning that drive today's most impressive AI results.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003ekey Features\u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eExplore the most advanced deep learning techniques that drive modern AI results\u003c\/li\u003e\n\u003cli\u003eImplement deep neural networks, autoencoders, GANs, VAEs, and deep reinforcement learning\u003c\/li\u003e\n\u003cli\u003eA wide study of GANs, including Improved GANs, Cross-Domain GANs, and Disentangled Representation GANs\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBook Description: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eRecent developments in deep learning, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Deep Reinforcement Learning (DRL) are creating impressive AI results in our news headlines - such as AlphaGo Zero beating world chess champions, and generative AI that can create art paintings that sell for over $400k because they are so human-like.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eAdvanced Deep Learning with Keras is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI. Using Keras as an open-source deep learning library, you'll find hands-on projects throughout that show you how to create more effective AI with the latest techniques.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eThe journey begins with an overview of MLPs, CNNs, and RNNs, which are the building blocks for the more advanced techniques in the book. You'll learn how to implement deep learning models with Keras and TensorFlow 1.x, and move forwards to advanced techniques, as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You then learn all about GANs, and how they can open new levels of AI performance. Next, you'll get up to speed with how VAEs are implemented, and you'll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans - a major stride forward for modern AI. To complete this set of advanced techniques, you'll learn how to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat You Will Learn: \u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eCutting-edge techniques in human-like AI performance\u003c\/li\u003e\n\u003cli\u003eImplement advanced deep learning models using Keras\u003c\/li\u003e\n\u003cli\u003eThe building blocks for advanced techniques - MLPs, CNNs, and RNNs\u003c\/li\u003e\n\u003cli\u003eDeep neural networks - ResNet and DenseNet\u003c\/li\u003e\n\u003cli\u003eAutoencoders and Variational Autoencoders (VAEs)\u003c\/li\u003e\n\u003cli\u003eGenerative Adversarial Networks (GANs) and creative AI techniques\u003c\/li\u003e\n\u003cli\u003eDisentangled Representation GANs, and Cross-Domain GANs\u003c\/li\u003e\n\u003cli\u003eDeep reinforcement learning methods and implementation\u003c\/li\u003e\n\u003cli\u003eProduce industry-standard applications using OpenAI Gym\u003c\/li\u003e\n\u003cli\u003eDeep Q-Learning and Policy Gradient Methods\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWho this book is for: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eSome fluency with Python is assumed. As an advanced book, you'll be familiar with some machine learning approaches, and some practical experience with DL will be helpful. Knowledge of Keras or TensorFlow 1.x is not required but would be helpful.;\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 368\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.76 x 9.25 x 7.5 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e October 31, 2018\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":43776373391495,"sku":"9781788629416","price":66.22,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0601\/2623\/2711\/files\/shHtZB6yA9781788629416.webp?v=1769248272","url":"https:\/\/booksby.splitshops.com\/products\/advanced-deep-learning-with-keras-apply-deep-learning-techniques-autoencoders-gans-variational-autoencoders-deep-reinforcement-learning-policy-g-paperback","provider":"Books by splitShops","version":"1.0","type":"link"}