{"product_id":"learn-keras-for-deep-neural-networks-a-fast-track-approach-to-modern-deep-learning-with-python-paperback","title":"Learn Keras for Deep Neural Networks: A Fast-Track Approach to Modern Deep Learning with Python - Paperback","description":"\u003cp\u003eby \u003cb\u003eJojo Moolayil\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eLearn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras.\u003c\/p\u003e \u003cp\u003eThe overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You'll tackle one use case for regression and another for classification leveraging popular Kaggle datasets. \u003c\/p\u003e \u003cp\u003eLater, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you'll further hone your skills in deep learning and cover areas of active development and research in deep learning. \u003c\/p\u003e \u003cp\u003eAt the end of \u003ci\u003eLearn Keras for Deep Neural Networks\u003c\/i\u003e, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eWhat You'll Learn\u003c\/b\u003e\u003c\/p\u003e \u003cli\u003eMaster fast-paced practical deep learning concepts with math- and programming-friendly abstractions.\u003c\/li\u003e \u003cli\u003eDesign, develop, train, validate, and deploy deep neural networks using the Keras framework\u003c\/li\u003e \u003cli\u003eUse best practices for debugging and validating deep learning models\u003c\/li\u003e \u003cli\u003eDeploy and integrate deep learning as a service into a larger software service or product\u003c\/li\u003e \u003cli\u003eExtend deep learning principles into other popular frameworks\u003c\/li\u003e \u003cp\u003e\u003cb\u003eWho This Book Is For \u003c\/b\u003e\u003c\/p\u003e Software engineers and data engineers with basic programming skills in any language and who are keen on exploring deep learning for a career move or an enterprise project.\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eLearn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras.\u003c\/p\u003e\u003cp\u003eThe overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You'll tackle one use case for regression and another for classification leveraging popular Kaggle datasets.\u003c\/p\u003e\u003cp\u003eLater, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you'll further hone your skills in deep learning and cover areas of active development and research in deep learning. \u003c\/p\u003e\u003cp\u003eAt the end of \u003ci\u003eLearn Keras for Deep Neural Networks\u003c\/i\u003e, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras.\u003c\/p\u003e\u003cp\u003eYou will: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eMaster fast-paced practical deep learning concepts with math- and programming-friendly abstractions.\u003c\/li\u003e\n\u003cli\u003eDesign, develop, train, validate, and deploy deep neural networks using the Keras framework\u003c\/li\u003e\n\u003cli\u003eUse best practices for debugging and validating deep learning models\u003c\/li\u003e\n\u003cli\u003eDeploy and integrate deep learning as a service into a larger software service or product\u003c\/li\u003e\n\u003cli\u003eExtend deep learning principles into other popular frameworks\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eJojo Moolayil is an artificial intelligence, deep learning, machine learning, and decision science professional with over five years of industrial experience and is a published author of the book Smarter Decisions - The Intersection of IoT and Decision Science. He has worked with several industry leaders on high-impact and critical data science and machine learning projects across multiple verticals. He is currently associated with Amazon Web Services as a research scientist. He was born and raised in Pune, India and graduated from the University of Pune with a major in Information Technology Engineering. He started his career with Mu Sigma Inc., the world's largest pure-play analytics provider and worked with the leaders of many Fortune 50 clients. He later worked with Flutura - an IoT analytics startup and GE. He currently resides in Vancouver, BC. Apart from writing books on decision science and IoT, Jojo has also been a technical reviewer for various books on machine learning, deep learning and business analytics with Apress and Packt publications. He is an active data science tutor and maintains a blog at http: \/\/blog.jojomoolayil.com.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 182\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.42 x 9.21 x 6.14 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e December 07, 2018\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":47432219918514,"sku":"9781484242391","price":48.58,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0770\/3891\/1666\/files\/f579b828b516b923946d551e0f3f727a.webp?v=1778601514","url":"https:\/\/box.dadyminds.org\/products\/learn-keras-for-deep-neural-networks-a-fast-track-approach-to-modern-deep-learning-with-python-paperback","provider":"DADYMINDS BOX","version":"1.0","type":"link"}