{"product_id":"9781617297717","title":"Machine Learning with TensorFlow, Second Edition","description":"\u003ctable\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eAuthor\/Contributor(s):\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eChris, Mattmann A.\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003ePublisher:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eManning\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eDate:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e2\/2\/2021\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eBinding:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003ePaperback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eCondition:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eNEW\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\u003cb\u003eUpdated with new code, new projects, and new chapters, \u003ci\u003eMachine Learning with TensorFlow, Second Edition\u003c\/i\u003e gives readers a solid foundation in machine-learning concepts and the TensorFlow library.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eSummary\u003c\/b\u003e\u003cbr\u003eUpdated with new code, new projects, and new chapters, \u003ci\u003eMachine Learning with TensorFlow, Second Edition\u003c\/i\u003e gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Written by NASA JPL Deputy CTO and Principal Data Scientist Chris Mattmann, all examples are accompanied by downloadable Jupyter Notebooks for a hands-on experience coding TensorFlow with Python. New and revised content expands coverage of core machine learning algorithms, and advancements in neural networks such as VGG-Face facial identification classifiers and deep speech classifiers.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the technology\u003c\/b\u003e\u003cbr\u003eSupercharge your data analysis with machine learning! ML algorithms automatically improve as they process data, so results get better over time. You don’t have to be a mathematician to use ML: Tools like Google’s TensorFlow library help with complex calculations so you can focus on getting the answers you need.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the book\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMachine Learning with TensorFlow, Second Edition\u003c\/i\u003e is a fully revised guide to building machine learning models using Python and TensorFlow. You’ll apply core ML concepts to real-world challenges, such as sentiment analysis, text classification, and image recognition. Hands-on examples illustrate neural network techniques for deep speech processing, facial identification, and auto-encoding with CIFAR-10.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eWhat's inside\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eMachine Learning with TensorFlow\u003cbr\u003eChoosing the best ML approaches\u003cbr\u003eVisualizing algorithms with TensorBoard\u003cbr\u003eSharing results with collaborators\u003cbr\u003eRunning models in Docker\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e\u003cbr\u003eRequires intermediate Python skills and knowledge of general algebraic concepts like vectors and matrices. Examples use the super-stable 1.15.x branch of TensorFlow and TensorFlow 2.x.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eChris Mattmann\u003c\/b\u003e is the Division Manager of the Artificial Intelligence, Analytics, and Innovation Organization at NASA Jet Propulsion Lab. The first edition of this book was written by \u003cb\u003eNishant Shukla\u003c\/b\u003e with \u003cb\u003eKenneth Fricklas\u003c\/b\u003e.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003ePART 1 - YOUR MACHINE-LEARNING RIG\u003cbr\u003e\u003cbr\u003e1 A machine-learning odyssey\u003cbr\u003e\u003cbr\u003e2 TensorFlow essentials\u003cbr\u003e\u003cbr\u003ePART 2 - CORE LEARNING ALGORITHMS\u003cbr\u003e\u003cbr\u003e3 Linear regression and beyond\u003cbr\u003e\u003cbr\u003e4 Using regression for call-center volume prediction\u003cbr\u003e\u003cbr\u003e5 A gentle introduction to classification\u003cbr\u003e\u003cbr\u003e6 Sentiment classification: Large movie-review dataset\u003cbr\u003e\u003cbr\u003e7 Automatically clustering data\u003cbr\u003e\u003cbr\u003e8 Inferring user activity from Android accelerometer data\u003cbr\u003e\u003cbr\u003e9 Hidden Markov models\u003cbr\u003e\u003cbr\u003e10 Part-of-speech tagging and word-sense disambiguation\u003cbr\u003e\u003cbr\u003ePART 3 - THE NEURAL NETWORK PARADIGM\u003cbr\u003e\u003cbr\u003e11 A peek into autoencoders\u003cbr\u003e\u003cbr\u003e12 Applying autoencoders: The CIFAR-10 image dataset\u003cbr\u003e\u003cbr\u003e13 Reinforcement learning\u003cbr\u003e\u003cbr\u003e14 Convolutional neural networks\u003cbr\u003e\u003cbr\u003e15 Building a real-world CNN: VGG-Face ad VGG-Face Lite\u003cbr\u003e\u003cbr\u003e16 Recurrent neural networks\u003cbr\u003e\u003cbr\u003e17 LSTMs and automatic speech recognition\u003cbr\u003e\u003cbr\u003e18 Sequence-to-sequence models for chatbots\u003cbr\u003e\u003cbr\u003e19 Utility landscape","brand":"Manning","offers":[{"title":"Default Title","offer_id":42973610672383,"sku":"9781617297717","price":49.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0452\/0886\/2873\/files\/Jacket_8895baaa-d70b-424f-a010-e79704f6a93c.jpg?v=1771352088","url":"https:\/\/massivebookshop.com\/products\/9781617297717","provider":"MASSIVE BOOKSHOP","version":"1.0","type":"link"}