How To Be Happier Teach Yourself New Edition Ebook Epub
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How to Be Happier: Teach Yourself (New Edition) Ebook Epub
Everyone wants to be happy. But it's a lot easier said than done. Most books on happiness are fixated on particular routes to happiness, such as wealth, NLP, positive thinking, or mindfulness. This book takes a more joined-up approach, using each of these techniques, but also including the latest scientific research on what really makes us happy (clue: it's not the things you think).
Happier
Author: Tal Ben-Shahar
language: en
Publisher: McGraw Hill Professional
Release Date: 2007-05-25
Can YouLearn to BeHappy? YES . . . according to the teacher of HarvardUniversity’s most popular and life-changingcourse. One out of every five Harvard studentshas lined up to hear Tal Ben-Shahar’sinsightful and inspiring lectures on thatever-elusive state: HAPPINESS. HOW? Grounded in the revolutionary “positive psychology” movement,Ben-Shahar ingeniously combines scientific studies, scholarly research, self-help advice, and spiritual enlightenment. He weaves them together into a set of principles that you can apply to your daily life. Once you open your heart and mind to Happier ’s thoughts, you will feel more fulfilled, more connected . . . and, yes, HAPPIER. “Dr. Ben-Shahar, one of the most popular teachers in Harvard’s recent history, has written a personal, informed, and highly enjoyable primer on how to become happier. It would be wise to take his advice.” --Ellen J. Langer, author of Mindfulness and On Becoming an Artist “This fine book shimmers with a rare brand of good sense that is imbedded in scientific knowledge about how to increase happiness. It is easy to see how this is the backbone of the most popular course at Harvard today." --Martin E. P. Seligman, author of Authentic Happiness
Machine Learning with TensorFlow, Second Edition
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