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Machine Learning with TensorFlow

Autor Chris Mattmann
en Limba Engleză Paperback – 15 mar 2021
Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Summary
Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition 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. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology
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. About the book
Machine Learning with TensorFlow, Second Edition 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. What's inside Machine Learning with TensorFlow
Choosing the best ML approaches
Visualizing algorithms with TensorBoard
Sharing results with collaborators
Running models in Docker About the reader
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. About the author
Chris Mattmann 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 Nishant Shukla with Kenneth Fricklas. Table of Contents PART 1 - YOUR MACHINE-LEARNING RIG 1 A machine-learning odyssey 2 TensorFlow essentials PART 2 - CORE LEARNING ALGORITHMS 3 Linear regression and beyond 4 Using regression for call-center volume prediction 5 A gentle introduction to classification 6 Sentiment classification: Large movie-review dataset 7 Automatically clustering data 8 Inferring user activity from Android accelerometer data 9 Hidden Markov models 10 Part-of-speech tagging and word-sense disambiguation PART 3 - THE NEURAL NETWORK PARADIGM 11 A peek into autoencoders 12 Applying autoencoders: The CIFAR-10 image dataset 13 Reinforcement learning 14 Convolutional neural networks 15 Building a real-world CNN: VGG-Face ad VGG-Face Lite 16 Recurrent neural networks 17 LSTMs and automatic speech recognition 18 Sequence-to-sequence models for chatbots 19 Utility landscape
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Specificații

ISBN-13: 9781617297717
ISBN-10: 1617297712
Pagini: 472
Dimensiuni: 190 x 235 x 25 mm
Greutate: 0.74 kg
Ediția:2 ed
Editura: Manning Publications

Cuprins

table of contents
PART 1. YOUR MACHINE-LEARNING RIG
READ IN LIVEBOOK1A MACHINE-LEARNING ODYSSEY
READ IN LIVEBOOK2TENSORFLOW ESSENTIALS
PART 2. CORE LEARNING ALGORITHMS
READ IN LIVEBOOK3LINEAR REGRESSION AND BEYOND
READ IN LIVEBOOK4USING REGRESSION FOR CALL-CENTER VOLUME PREDICTION
READ IN LIVEBOOK5A GENTLE INTRODUCTION TO CLASSIFICATION
READ IN LIVEBOOK6SENTIMENT CLASSIFICATION: LARGE MOVIE-REVIEW DATASET
READ IN LIVEBOOK7AUTOMATICALLY CLUSTERING DATA
READ IN LIVEBOOK8INFERRING USER ACTIVITY FROM ANDROID ACCELEROMETER DATA
READ IN LIVEBOOK9HIDDEN MARKOV MODELS
READ IN LIVEBOOK10PART-OF-SPEECH TAGGING AND WORD-SENSE DISAMBIGUATION
PART 3. THE NEURAL NETWORK PARADIGM
READ IN LIVEBOOK11A PEEK INTO AUTOENCODERS
READ IN LIVEBOOK12APPLYING AUTOENCODERS: THE CIFAR-10 IMAGE DATASET
READ IN LIVEBOOK13REINFORCEMENT LEARNING
READ IN LIVEBOOK14CONVOLUTIONAL NEURAL NETWORKS
READ IN LIVEBOOK15BUILDING A REAL-WORLD CNN: VGG -FACE AND VGG -FACE LITE
READ IN LIVEBOOK16RECURRENT NEURAL NETWORKS
READ IN LIVEBOOK17LSTMS AND AUTOMATIC SPEECH RECOGNITION
READ IN LIVEBOOK18SEQUENCE-TO-SEQUENCE MODELS FOR CHATBOTS
READ IN LIVEBOOK19UTILITY LANDSCAPE
READ IN LIVEBOOKAPPENDIX A: INSTALLATION INSTRUCTIONS