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Professor Morse Brulay is writing to us from his jail cell in Ecuador. Brulay is a roamer and a seeker of the mysterious, the new, the erotic but also of enduring wisdom. In his search for the Deep Code the secret of time travel he runs afoul of the Fomors, the dark spirits of the Druidic world. Cursed with terrifying ashbacks to his prehistoric past, Morse loses his moral bearings and ees to the Amazon jungle in search of spiritual rebirth. There among the Indians he finds the knowledge he seeks and is joined in his new vision by Lotte, the beautiful biologist. In their effort to protect and nourish this knowledge, Morse and Lotte now nd their own lives in danger. Only the power of the discovered wisdom will determine what happens to them, and to the wisdom itself.
Book Synopsis Deep Code by : Christie W. Kiefer
Download or read book Deep Code written by Christie W. Kiefer and published by iUniverse. This book was released on 2011-06-29 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Professor Morse Brulay is writing to us from his jail cell in Ecuador. Brulay is a roamer and a seeker of the mysterious, the new, the erotic but also of enduring wisdom. In his search for the Deep Code the secret of time travel he runs afoul of the Fomors, the dark spirits of the Druidic world. Cursed with terrifying ashbacks to his prehistoric past, Morse loses his moral bearings and ees to the Amazon jungle in search of spiritual rebirth. There among the Indians he finds the knowledge he seeks and is joined in his new vision by Lotte, the beautiful biologist. In their effort to protect and nourish this knowledge, Morse and Lotte now nd their own lives in danger. Only the power of the discovered wisdom will determine what happens to them, and to the wisdom itself.
“We finally have the definitive treatise on PyTorch! It covers the basics and abstractions in great detail. I hope this book becomes your extended reference document.” —Soumith Chintala, co-creator of PyTorch Key Features Written by PyTorch’s creator and key contributors Develop deep learning models in a familiar Pythonic way Use PyTorch to build an image classifier for cancer detection Diagnose problems with your neural network and improve training with data augmentation Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About The Book Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands. Instantly familiar to anyone who knows Python data tools like NumPy and Scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s great for building quick models, and it scales smoothly from laptop to enterprise. Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated. All code samples are easy to explore in downloadable Jupyter notebooks. What You Will Learn Understanding deep learning data structures such as tensors and neural networks Best practices for the PyTorch Tensor API, loading data in Python, and visualizing results Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Methods for training networks with limited inputs Sifting through unreliable results to diagnose and fix problems in your neural network Improve your results with augmented data, better model architecture, and fine tuning This Book Is Written For For Python programmers with an interest in machine learning. No experience with PyTorch or other deep learning frameworks is required. About The Authors Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer. Table of Contents PART 1 - CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 - LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 - DEPLOYMENT 15 Deploying to production
Book Synopsis Deep Learning with PyTorch by : Luca Pietro Giovanni Antiga
Download or read book Deep Learning with PyTorch written by Luca Pietro Giovanni Antiga and published by Simon and Schuster. This book was released on 2020-07-01 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: “We finally have the definitive treatise on PyTorch! It covers the basics and abstractions in great detail. I hope this book becomes your extended reference document.” —Soumith Chintala, co-creator of PyTorch Key Features Written by PyTorch’s creator and key contributors Develop deep learning models in a familiar Pythonic way Use PyTorch to build an image classifier for cancer detection Diagnose problems with your neural network and improve training with data augmentation Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About The Book Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands. Instantly familiar to anyone who knows Python data tools like NumPy and Scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s great for building quick models, and it scales smoothly from laptop to enterprise. Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated. All code samples are easy to explore in downloadable Jupyter notebooks. What You Will Learn Understanding deep learning data structures such as tensors and neural networks Best practices for the PyTorch Tensor API, loading data in Python, and visualizing results Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Methods for training networks with limited inputs Sifting through unreliable results to diagnose and fix problems in your neural network Improve your results with augmented data, better model architecture, and fine tuning This Book Is Written For For Python programmers with an interest in machine learning. No experience with PyTorch or other deep learning frameworks is required. About The Authors Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer. Table of Contents PART 1 - CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 - LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 - DEPLOYMENT 15 Deploying to production
Summary Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game. Foreword by Thore Graepel, DeepMind Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The ancient strategy game of Go is an incredible case study for AI. In 2016, a deep learning-based system shocked the Go world by defeating a world champion. Shortly after that, the upgraded AlphaGo Zero crushed the original bot by using deep reinforcement learning to master the game. Now, you can learn those same deep learning techniques by building your own Go bot! About the Book Deep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. As you progress, you'll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You'll enjoy watching your bot master the game of Go, and along the way, you'll discover how to apply your new deep learning skills to a wide range of other scenarios! What's inside Build and teach a self-improving game AI Enhance classical game AI systems with deep learning Implement neural networks for deep learning About the Reader All you need are basic Python skills and high school-level math. No deep learning experience required. About the Author Max Pumperla and Kevin Ferguson are experienced deep learning specialists skilled in distributed systems and data science. Together, Max and Kevin built the open source bot BetaGo. Table of Contents PART 1 - FOUNDATIONS Toward deep learning: a machine-learning introduction Go as a machine-learning problem Implementing your first Go bot PART 2 - MACHINE LEARNING AND GAME AI Playing games with tree search Getting started with neural networks Designing a neural network for Go data Learning from data: a deep-learning bot Deploying bots in the wild Learning by practice: reinforcement learning Reinforcement learning with policy gradients Reinforcement learning with value methods Reinforcement learning with actor-critic methods PART 3 - GREATER THAN THE SUM OF ITS PARTS AlphaGo: Bringing it all together AlphaGo Zero: Integrating tree search with reinforcement learning
Book Synopsis Deep Learning and the Game of Go by : Kevin Ferguson
Download or read book Deep Learning and the Game of Go written by Kevin Ferguson and published by Simon and Schuster. This book was released on 2019-01-06 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game. Foreword by Thore Graepel, DeepMind Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The ancient strategy game of Go is an incredible case study for AI. In 2016, a deep learning-based system shocked the Go world by defeating a world champion. Shortly after that, the upgraded AlphaGo Zero crushed the original bot by using deep reinforcement learning to master the game. Now, you can learn those same deep learning techniques by building your own Go bot! About the Book Deep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. As you progress, you'll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You'll enjoy watching your bot master the game of Go, and along the way, you'll discover how to apply your new deep learning skills to a wide range of other scenarios! What's inside Build and teach a self-improving game AI Enhance classical game AI systems with deep learning Implement neural networks for deep learning About the Reader All you need are basic Python skills and high school-level math. No deep learning experience required. About the Author Max Pumperla and Kevin Ferguson are experienced deep learning specialists skilled in distributed systems and data science. Together, Max and Kevin built the open source bot BetaGo. Table of Contents PART 1 - FOUNDATIONS Toward deep learning: a machine-learning introduction Go as a machine-learning problem Implementing your first Go bot PART 2 - MACHINE LEARNING AND GAME AI Playing games with tree search Getting started with neural networks Designing a neural network for Go data Learning from data: a deep-learning bot Deploying bots in the wild Learning by practice: reinforcement learning Reinforcement learning with policy gradients Reinforcement learning with value methods Reinforcement learning with actor-critic methods PART 3 - GREATER THAN THE SUM OF ITS PARTS AlphaGo: Bringing it all together AlphaGo Zero: Integrating tree search with reinforcement learning
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Book Synopsis Deep Learning for Coders with fastai and PyTorch by : Jeremy Howard
Download or read book Deep Learning for Coders with fastai and PyTorch written by Jeremy Howard and published by O'Reilly Media. This book was released on 2020-06-29 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance
Book Synopsis Deep Learning with Python by : Francois Chollet
Download or read book Deep Learning with Python written by Francois Chollet and published by Simon and Schuster. This book was released on 2017-11-30 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance
Laxton's gives you access to the most reliable and current data. All 250,000 price elements have been individually checked and updated for the 2002 edition so that your estimates are always accurate and cost competitive. Laxton's makes analytical estimating simple and straightforward by displaying a complete breakdown for all measured items under 10 separate headings, all on a single page. This shows you a complete price build-up at a glance - and gives you the option to make price adjustments wherever necessary. You can find the sections you need quickly and easily, via the special marker system on the front cover and page edges. The free CD with this price book contains Masterbill's ESTIMATOR software and fully resourced data on all the price elements in Laxtons. Not only does the CD offer fast and efficient pricing at the touch of a button, it gives details of all the resources required to do the job. Laxton's approximate estimating section gives all in pricing for quick reference on the cost of composite items such as floors helping you calculate the cost implications of using plywood sheeting rather than softwood boarding, for example. Laxton's Basic Price section gives you a quick price on hundreds of items - from concrete work to roofing materials - to save you going through hundreds of lists from suppliers, manufacturers and building merchants. Laxton's Brand and Trade Names section lists over 12,000 brands and trade names and company addresses to help you locate specific items. Latest wage rates, fees and allowances All 250,000 price elements checked and updated
Book Synopsis Laxton's Building Price Book 2002 by : V B Johnson
Download or read book Laxton's Building Price Book 2002 written by V B Johnson and published by Elsevier. This book was released on 2001-10-10 with total page 817 pages. Available in PDF, EPUB and Kindle. Book excerpt: Laxton's gives you access to the most reliable and current data. All 250,000 price elements have been individually checked and updated for the 2002 edition so that your estimates are always accurate and cost competitive. Laxton's makes analytical estimating simple and straightforward by displaying a complete breakdown for all measured items under 10 separate headings, all on a single page. This shows you a complete price build-up at a glance - and gives you the option to make price adjustments wherever necessary. You can find the sections you need quickly and easily, via the special marker system on the front cover and page edges. The free CD with this price book contains Masterbill's ESTIMATOR software and fully resourced data on all the price elements in Laxtons. Not only does the CD offer fast and efficient pricing at the touch of a button, it gives details of all the resources required to do the job. Laxton's approximate estimating section gives all in pricing for quick reference on the cost of composite items such as floors helping you calculate the cost implications of using plywood sheeting rather than softwood boarding, for example. Laxton's Basic Price section gives you a quick price on hundreds of items - from concrete work to roofing materials - to save you going through hundreds of lists from suppliers, manufacturers and building merchants. Laxton's Brand and Trade Names section lists over 12,000 brands and trade names and company addresses to help you locate specific items. Latest wage rates, fees and allowances All 250,000 price elements checked and updated
Pierre Bourdieu: The Last Musketeer of the French Revolution argues that Bourdieu appointed himself as the representative of the French people and acted as its National Assembly. In that capacity, he set himself to work with the charter of the preamble toThe Declaration of the Rights of Man and the Citizen to remind the members of the social body of their rights and obligations; to monitor the legislative and executive powers and compare them with the Republican purposes of ideal political and social agendas decreed by the revolutionaries of 1789; and, overall, to maintain the tenets of the French constitution. In that sense, like d'Artagnan in Dumas'The Three Musketeers, Bourdieu took it upon himself to be the fighter for true France, namely the keeper of the Republican tradition of the French Revolution. Bourdieu's entire oeuvre was indeed motivated by the failed promise of the French Revolution and by the demise of its most noble ideals. His passionate analyses--of educational stratification, cultural production and consumption, gender relations, the social structure of the economy, and the effects of globalization--were always carried out with the moral benchmark of the revolution in mind. Bourdieu was indeed passionately tied to the values of the French Revolution, notably to liberty and meritocracy, to social equality and to the democratization and universalization of government. But wherever he looked, he saw those values betrayed by the very people who argued for their implementation, and by the governmental bodies which were devised in order to guarantee their effectiveness. Committed to the values of the Declaration, he was constantly frustrated by the betrayals of universalization by the Fifth Republic.
Book Synopsis Pierre Bourdieu by : Gad Yaʼir
Download or read book Pierre Bourdieu written by Gad Yaʼir and published by Rowman & Littlefield. This book was released on 2009 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pierre Bourdieu: The Last Musketeer of the French Revolution argues that Bourdieu appointed himself as the representative of the French people and acted as its National Assembly. In that capacity, he set himself to work with the charter of the preamble toThe Declaration of the Rights of Man and the Citizen to remind the members of the social body of their rights and obligations; to monitor the legislative and executive powers and compare them with the Republican purposes of ideal political and social agendas decreed by the revolutionaries of 1789; and, overall, to maintain the tenets of the French constitution. In that sense, like d'Artagnan in Dumas'The Three Musketeers, Bourdieu took it upon himself to be the fighter for true France, namely the keeper of the Republican tradition of the French Revolution. Bourdieu's entire oeuvre was indeed motivated by the failed promise of the French Revolution and by the demise of its most noble ideals. His passionate analyses--of educational stratification, cultural production and consumption, gender relations, the social structure of the economy, and the effects of globalization--were always carried out with the moral benchmark of the revolution in mind. Bourdieu was indeed passionately tied to the values of the French Revolution, notably to liberty and meritocracy, to social equality and to the democratization and universalization of government. But wherever he looked, he saw those values betrayed by the very people who argued for their implementation, and by the governmental bodies which were devised in order to guarantee their effectiveness. Committed to the values of the Declaration, he was constantly frustrated by the betrayals of universalization by the Fifth Republic.
Summary Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning. About the Book Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks. What's inside The science behind deep learning Building and training your own neural networks Privacy concepts, including federated learning Tips for continuing your pursuit of deep learning About the Reader For readers with high school-level math and intermediate programming skills. About the Author Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform. Table of Contents Introducing deep learning: why you should learn it Fundamental concepts: how do machines learn? Introduction to neural prediction: forward propagation Introduction to neural learning: gradient descent Learning multiple weights at a time: generalizing gradient descent Building your first deep neural network: introduction to backpropagation How to picture neural networks: in your head and on paper Learning signal and ignoring noise:introduction to regularization and batching Modeling probabilities and nonlinearities: activation functions Neural learning about edges and corners: intro to convolutional neural networks Neural networks that understand language: king - man + woman == ? Neural networks that write like Shakespeare: recurrent layers for variable-length data Introducing automatic optimization: let's build a deep learning framework Learning to write like Shakespeare: long short-term memory Deep learning on unseen data: introducing federated learning Where to go from here: a brief guide
Book Synopsis Grokking Deep Learning by : Andrew W. Trask
Download or read book Grokking Deep Learning written by Andrew W. Trask and published by Simon and Schuster. This book was released on 2019-01-23 with total page 475 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning. About the Book Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks. What's inside The science behind deep learning Building and training your own neural networks Privacy concepts, including federated learning Tips for continuing your pursuit of deep learning About the Reader For readers with high school-level math and intermediate programming skills. About the Author Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform. Table of Contents Introducing deep learning: why you should learn it Fundamental concepts: how do machines learn? Introduction to neural prediction: forward propagation Introduction to neural learning: gradient descent Learning multiple weights at a time: generalizing gradient descent Building your first deep neural network: introduction to backpropagation How to picture neural networks: in your head and on paper Learning signal and ignoring noise:introduction to regularization and batching Modeling probabilities and nonlinearities: activation functions Neural learning about edges and corners: intro to convolutional neural networks Neural networks that understand language: king - man + woman == ? Neural networks that write like Shakespeare: recurrent layers for variable-length data Introducing automatic optimization: let's build a deep learning framework Learning to write like Shakespeare: long short-term memory Deep learning on unseen data: introducing federated learning Where to go from here: a brief guide
What is the secret of talent? How do we unlock it? This groundbreaking work provides readers with tools they can use to maximize potential in themselves and others. Whether you’re coaching soccer or teaching a child to play the piano, writing a novel or trying to improve your golf swing, this revolutionary book shows you how to grow talent by tapping into a newly discovered brain mechanism. Drawing on cutting-edge neurology and firsthand research gathered on journeys to nine of the world’s talent hotbeds—from the baseball fields of the Caribbean to a classical-music academy in upstate New York—Coyle identifies the three key elements that will allow you to develop your gifts and optimize your performance in sports, art, music, math, or just about anything. • Deep Practice Everyone knows that practice is a key to success. What everyone doesn’t know is that specific kinds of practice can increase skill up to ten times faster than conventional practice. • Ignition We all need a little motivation to get started. But what separates truly high achievers from the rest of the pack? A higher level of commitment—call it passion—born out of our deepest unconscious desires and triggered by certain primal cues. Understanding how these signals work can help you ignite passion and catalyze skill development. • Master Coaching What are the secrets of the world’s most effective teachers, trainers, and coaches? Discover the four virtues that enable these “talent whisperers” to fuel passion, inspire deep practice, and bring out the best in their students. These three elements work together within your brain to form myelin, a microscopic neural substance that adds vast amounts of speed and accuracy to your movements and thoughts. Scientists have discovered that myelin might just be the holy grail: the foundation of all forms of greatness, from Michelangelo’s to Michael Jordan’s. The good news about myelin is that it isn’t fixed at birth; to the contrary, it grows, and like anything that grows, it can be cultivated and nourished. Combining revelatory analysis with illuminating examples of regular people who have achieved greatness, this book will not only change the way you think about talent, but equip you to reach your own highest potential.
Book Synopsis The Talent Code by : Daniel Coyle
Download or read book The Talent Code written by Daniel Coyle and published by Bantam. This book was released on 2009-04-28 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: What is the secret of talent? How do we unlock it? This groundbreaking work provides readers with tools they can use to maximize potential in themselves and others. Whether you’re coaching soccer or teaching a child to play the piano, writing a novel or trying to improve your golf swing, this revolutionary book shows you how to grow talent by tapping into a newly discovered brain mechanism. Drawing on cutting-edge neurology and firsthand research gathered on journeys to nine of the world’s talent hotbeds—from the baseball fields of the Caribbean to a classical-music academy in upstate New York—Coyle identifies the three key elements that will allow you to develop your gifts and optimize your performance in sports, art, music, math, or just about anything. • Deep Practice Everyone knows that practice is a key to success. What everyone doesn’t know is that specific kinds of practice can increase skill up to ten times faster than conventional practice. • Ignition We all need a little motivation to get started. But what separates truly high achievers from the rest of the pack? A higher level of commitment—call it passion—born out of our deepest unconscious desires and triggered by certain primal cues. Understanding how these signals work can help you ignite passion and catalyze skill development. • Master Coaching What are the secrets of the world’s most effective teachers, trainers, and coaches? Discover the four virtues that enable these “talent whisperers” to fuel passion, inspire deep practice, and bring out the best in their students. These three elements work together within your brain to form myelin, a microscopic neural substance that adds vast amounts of speed and accuracy to your movements and thoughts. Scientists have discovered that myelin might just be the holy grail: the foundation of all forms of greatness, from Michelangelo’s to Michael Jordan’s. The good news about myelin is that it isn’t fixed at birth; to the contrary, it grows, and like anything that grows, it can be cultivated and nourished. Combining revelatory analysis with illuminating examples of regular people who have achieved greatness, this book will not only change the way you think about talent, but equip you to reach your own highest potential.
With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. You’ll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way. Author Seth Weidman shows you how neural networks work using a first principles approach. You’ll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, you’ll be set up for success on all future deep learning projects. This book provides: Extremely clear and thorough mental models—accompanied by working code examples and mathematical explanations—for understanding neural networks Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented framework Working implementations and clear-cut explanations of convolutional and recurrent neural networks Implementation of these neural network concepts using the popular PyTorch framework
Book Synopsis Deep Learning from Scratch by : Seth Weidman
Download or read book Deep Learning from Scratch written by Seth Weidman and published by O'Reilly Media. This book was released on 2019-09-09 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. You’ll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way. Author Seth Weidman shows you how neural networks work using a first principles approach. You’ll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, you’ll be set up for success on all future deep learning projects. This book provides: Extremely clear and thorough mental models—accompanied by working code examples and mathematical explanations—for understanding neural networks Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented framework Working implementations and clear-cut explanations of convolutional and recurrent neural networks Implementation of these neural network concepts using the popular PyTorch framework
