Machine Learning with Tensorflow, Second Edition - by Mattmann A Chris (Paperback)
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<p/><br></br><p><b> Book Synopsis </b></p></br></br><b>Updated with new code, new projects, and new chapters, <i>Machine Learning with TensorFlow, Second Edition</i> gives readers a solid foundation in machine-learning concepts and the TensorFlow library.</b> <p/><b>Summary</b><br> Updated with new code, new projects, and new chapters, <i>Machine Learning with TensorFlow, Second Edition</i> 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. <p/> Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. <p/> <b>About the technology</b><br> Supercharge 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. <p/> <b>About the book</b><br> <i>Machine Learning with TensorFlow, Second Edition</i> 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. <p/> <b>What's inside</b> <p/> Machine Learning with TensorFlow<br> Choosing the best ML approaches<br> Visualizing algorithms with TensorBoard<br> Sharing results with collaborators<br> Running models in Docker <p/> <b>About the reader</b><br> Requires 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. <p/> <b>About the author</b><br> <b>Chris Mattmann</b> 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 <b>Nishant Shukla</b> with <b>Kenneth Fricklas</b>. <p/> Table of Contents <p/> PART 1 - YOUR MACHINE-LEARNING RIG <p/> 1 A machine-learning odyssey <p/> 2 TensorFlow essentials <p/> PART 2 - CORE LEARNING ALGORITHMS <p/> 3 Linear regression and beyond <p/> 4 Using regression for call-center volume prediction <p/> 5 A gentle introduction to classification <p/> 6 Sentiment classification: Large movie-review dataset <p/> 7 Automatically clustering data <p/> 8 Inferring user activity from Android accelerometer data <p/> 9 Hidden Markov models <p/> 10 Part-of-speech tagging and word-sense disambiguation <p/> PART 3 - THE NEURAL NETWORK PARADIGM <p/> 11 A peek into autoencoders <p/> 12 Applying autoencoders: The CIFAR-10 image dataset <p/> 13 Reinforcement learning <p/> 14 Convolutional neural networks <p/> 15 Building a real-world CNN: VGG-Face ad VGG-Face Lite <p/> 16 Recurrent neural networks <p/> 17 LSTMs and automatic speech recognition <p/> 18 Sequence-to-sequence models for chatbots <p/> 19 Utility landscape<p/><br></br><p><b> About the Author </b></p></br></br><b>Chris Mattmann</b> is the Deputy Chief Technology and Innovation Officer at NASA Jet Propulsion Lab, where he has been recognised as JPL's first Principal Scientist in the area of Data Science. Chris has applied TensorFlow to challenges he's faced at NASA, including building an implementation of Google's Show & Tell algorithm for image captioning using TensorFlow. He contributes to open source as a former Director at the Apache Software Foundation, and teaches graduate courses at USC in Content Detection and Analysis, and in Search Engines and Information Retrieval.<br>
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