Now that machine learning is thriving, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you.This hands-on book shows you how to use:Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry pointTensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networksPractical code examples that you can apply without learning excessive machine learning theory or algorithm details

    It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.

    Weil comes the first book in the Originator Wars series. The Lost Fleets have settled on an Originator Dyson Sphere in preparation for their greatest test yet. A great enemy is spreading across the universe conquering galaxy after galaxy. The few surviving Originators have asked Fleet Admiral Jeremy Strong to fight a war against their most hated enemy, the Anti-Life. If he fails to stop their advance, then the entire universe could be in danger. Rear Admiral Kathryn Barnes sets out on a desperate quest. She must find the missing Originators who fled into deep space millions of years in the past to escape a deadly disease. If they are to have any hope of winning this intergalactic war, the missing Originators must be found.

    Under its guidance we have spread amongst the stars and experienced an unprecedented age of peace, prosperity, and technological advancement. All of that is about the change.While on a covert mission to spy on the Krixis, a telepathic alien race, Empathic Services agent Eyana Ora uncovers a plot to destroy all mankind. She launches a desperate bid to stop a group of insurgents from obtaining a secret super-weapon stored within an Ancient outpost on world sacred to the Krixis.Gav Gendin is an archaeologist obsessed with the Ancients, an extinct race of highly advanced aliens. After years of searching, he locates one of their temples on an abandoned Krixis world. But when it turns out the system is guarded, his research expedition becomes a gamble that could cost him his life.Neither one of them has a hope of accomplishing their missions without Silky, a snarky neural-interfacing AI companion. It's his job to piece together the secrets they each unearth, secrets that will shape humanity's future.

    Autonomous maintenance robots take on a much larger role in saving a spaceship from aliens than the ship's human crew could have ever suspected.A science fiction story first published in Clarkesworld, Issue 132, September 2017.

     If you have ever found yourself lost halfway through other introductory materials on this topic, this is the book for you. If you don't understand set terminology such as vectors, hyperplanes, and centroids, then this is also the book for you. This starter course isn't a picture story book but does include many visual examples that break algorithms down into a digestible and practical format. As a starter course, this book connects the dots and offers the crash course I wish I had when I first started. The kind of guide I wish had before I started taking on introductory courses that presume you’re two days away from an advanced mathematics exam. That’s why this introductory course doesn’t go further on the subject than other introductory books, but rather, goes a step back. A half-step back in order to help everyone make his or her first strides in machine learning and is an ideal study companion for the visual learner. In this step-by-step guide you will learn: - How to download free datasets - What tools and software packages you need - Data scrubbing techniques, including one-hot encoding, binning and dealing with missing data - Preparing data for analysis, including k-fold Validation - Regression analysis to create trend lines - Clustering, including k-means and k-nearest Neighbors - Naive Bayes Classifier to predict new classes - Anomaly detection and SVM algorithms to combat anomalies and outliers - The basics of Neural Networks - Bias/Variance to improve your machine learning model - Decision Trees to decode classification Please feel welcome to join this starter course by buying a copy, or sending a free sample to your preferred device.

    Loosely based on neuron behavior inside of human brains, these systems are rapidly catching up with the intelligence of their human creators, defeating the world champion Go player, achieving superhuman performance on video games, driving cars, translating languages, and sometimes even helping law enforcement fight crime. Deep Learning is a revolution that is changing every industry across the globe.Grokking Deep Learning is the perfect place to begin your deep learning journey. Rather than just learn the “black box” API of some library or framework, you will actually understand how to build these algorithms completely from scratch. You will understand how Deep Learning is able to learn at levels greater than humans. You will be able to understand the “brain” behind state-of-the-art Artificial Intelligence. Furthermore, unlike other courses that assume advanced knowledge of Calculus and leverage complex mathematical notation, if you’re a Python hacker who passed high-school algebra, you’re ready to go. And at the end, you’ll even build an A.I. that will learn to defeat you in a classic Atari game.

    Table of Contents:PrefaceAcknowledgmentsIntroductionLearning Basics and Linear ModelsFrom Linear Models to Multi-layer PerceptronsFeed-forward Neural NetworksNeural Network TrainingFeatures for Textual DataCase Studies of NLP FeaturesFrom Textual Features to InputsLanguage ModelingPre-trained Word RepresentationsUsing Word EmbeddingsCase Study: A Feed-forward Architecture for Sentence Meaning InferenceNgram Detectors: Convolutional Neural NetworksRecurrent Neural Networks: Modeling Sequences and StacksConcrete Recurrent Neural Network ArchitecturesModeling with Recurrent NetworksConditioned GenerationModeling Trees with Recursive Neural NetworksStructured Output PredictionCascaded, Multi-task and Semi-supervised LearningConclusionBibliographyAuthor's Biography

    You'll even solve fun and interesting real-world problems using deep learning along the way.

    Now they’ve written a guide to help readers make the most of our collective future. Machine | Platform | Crowd outlines the opportunities and challenges inherent in the science fiction technologies that have come to life in recent years, like self-driving cars and 3D printers, online platforms for renting outfits and scheduling workouts, or crowd-sourced medical research and financial instruments.

     Tane, a hydroponics farmer with some mad cereal crop gene-splicing skills, decides to get chipped. The operation gives him full control over his autonomic nervous and endocrine systems, plus the ability to install custom memories. All seems well until a couple of days later aliens come knocking at his door. And they aren't the friendly type. Soon Tane finds himself on a frenzied flight across the galaxy with a woman who can warp the very fabric of spacetime, her bodyguard--who’d just as soon kill Tane than protect him--and a starship that calls him snarky pet names. He's on the run not simply from the aliens but the whole damn human space navy. He only wished he knew why. Unfortunately for Tane, the answer might just destroy him. Not to mention the entire known universe.

    They make web searches better, organize photos, and are even used in speech translation. Heck, they can even generate encryption. At the same time, they are also mysterious and mind-bending: how exactly do they accomplish these things ? What goes on inside a neural network? On a high level, a network learns just like we do, through trial and error. This is true regardless if the network is supervised, unsupervised, or semi-supervised. Once we dig a bit deeper though, we discover that a handful of mathematical functions play a major role in the trial and error process. It also becomes clear that a grasp of the underlying mathematics helps clarify how a network learns. In the following chapters we will unpack the mathematics that drive a neural network. To do this, we will use a feedforward network as our model and follow input as it moves through the network.

    It sent the signal. Now our computers are killing us, helping the enemy drive us into extinction. But some of us refuse to die. We fight back. We learn. Jon Hawkins revives from cryogenic sleep in a drifting SLN battleship. The crew is dead and the main computer has been destroyed. Jon is a soldier, the start of the resistance, the one man with the will to beat the alien death machines that have terminated a thousand races. This is our hour as we face the ultimate evil, the galactic destroyer of life.

    It doesn't shy away from the full range of viewpoints or from the most controversial issues--from superintelligence to meaning, consciousness and the ultimate physical limits on life in the cosmos.

    In fact, his main concern is "How long until the government finds me?" Now the owner of a Mobile Network Droid and enrolled as a beta-tester for a brand new extraterrestrial package delivery system, Zane Pendon and Rob, a self-aware advertising drone, have to navigate the intricacies of shopping at home while avoiding arousing any suspicion from the Solar Alliance of Independent Planets and the US government, both of which are very interested in recovering the wayward piece of technology. However, it won't be long before worlds collide and mankind meets alien life. But with their status as intergalactic fugitives, Zane and Rob have their work cut out for them trying to stay alive and under the radar. Can alien infomercials save the world? Of course not. But they just might save Zane and his family.

    Chloe, as Izzy dubs her, is wonderfully supportive and helpful. Despite knowing that Chloe isn’t a real person, Izzy falls in love with the voice in her head. Can a relationship with an AI be as meaningful as one with a person? Is Izzy destined for heartbreak? The Voice in My Head is a lesbian sci-fi romance. It’s approximately 14,000 words long.

    It summarizes various techniques tested by major technology, advertising, and retail companies, and it glues these methods together with economic theory and machine learning. The book covers the main areas of marketing that require programmatic micro-decisioning - targeted promotions and advertisements, eCommerce search, recommendations, pricing, and assortment optimization."A comprehensive and indispensable reference for anyone undertaking the transformational journey towards algorithmic marketing."―Ali Bouhouch, CTO, Sephora Americas"It is a must-read for both data scientists and marketing officers―even better if they read it together."―Andrey Sebrant, Director of Strategic Marketing, Yandex"The book gives the executives, middle managers, and data scientists in your organization a set of concrete, actionable, and incremental recommendations on how to build better insights and decisions, starting today, one step at a time."―Victoria Livschitz, founder and CTO, Grid DynamicsTable of ContentsChapter 1 - IntroductionThe Subject of Algorithmic Marketing The Definition of Algorithmic Marketing Historical Backgrounds and Context Programmatic Services Who Should Read This Book? Summary Chapter 2 - Review of Predictive ModelingDescriptive, Predictive, and Prescriptive Analytics Economic Optimization Machine Learning Supervised Learning Representation Learning More Specialized Models Summary Chapter 3 - Promotions and AdvertisementsEnvironment Business Objectives Targeting Pipeline Response Modeling and Measurement Building Blocks: Targeting and LTV Models Designing and Running Campaigns Resource Allocation Online Advertisements Measuring the Effectiveness Architecture of Targeting Systems Summary Chapter 4 - SearchEnvironment Business Objectives Building Blocks: Matching and Ranking Mixing Relevance Signals Semantic Analysis Search Methods for Merchandising Relevance Tuning Architecture of Merchandising Search Services Summary Chapter 5 - RecommendationsEnvironment Business Objectives Quality Evaluation Overview of Recommendation Methods Content-based Filtering Introduction to Collaborative Filtering Neighborhood-based Collaborative Filtering Model-based Collaborative Filtering Hybrid Methods Contextual Recommendations Non-Personalized Recommendations Multiple Objective Optimization Architecture of Recommender Systems Summary Chapter 6 - Pricing and AssortmentEnvironment The Impact of Pricing Price and Value Price and Demand Basic Price Structures Demand Prediction Price Optimization Resource Allocation Assortment Optimization Architecture of Price Management Systems Summary

    A step-by-step visual journey through the mathematics of neural networks, and making your own using Python and Tensorflow.

    Trying to understand why, Carl tries to soothe him. Neighbors gather in front of Carl’s apartment to help – until they see him. The crowd cowers back, afraid of this monster. Carl runs. His life of luxury is ripped away. Forced beyond the city limits, Carl sees a land bereft of life. Traveling in search of answers, his quest comes to a sudden halt when he collapses. As darkness shrouds him, a figure hovers from above. Traveling along the same route, Eva Thomspon finds Carl and nurtures him back to life. Together, they continue the journey, finding out that their lives have too much in common to be a coincidence. As their affection for each other deepens, an unknown nemesis attempts to remove their only source of happiness – their love for each other.Interpretation is a dystopian fiction that explores hope and happiness in the bleakest of conditions and what happens when it’s torn away.

    The big picture of arti cial intelligence and machine learning—past, present, and future.Part 2.1: Supervised Learning. Learning with an answer key. Introducing linear regression, loss functions, over tting, and gradient descent.Part 2.2: Supervised Learning II. Two methods of classi cation: logistic regression and support vector machines (SVMs).Part 2.3: Supervised Learning III. Non-parametric learners: k-nearest neighbors, decision trees, random forests. Introducing cross-validation, hyperparameter tuning, and ensemble models.Part 3: Unsupervised Learning. Clustering: k-means, hierarchical. Dimensionality reduction: principal components analysis (PCA), singular value decomposition (SVD).Part 4: Neural Networks & Deep Learning. Why, where, and how deep learning works. Drawing inspiration from the brain. Convolutional neural networks (CNNs), recurrent neural networks (RNNs). Real-world applications.Part 5: Reinforcement Learning. Exploration and exploitation. Markov decision processes. Q-learning, policy learning, and deep reinforcement learning. The value learning problem.Appendix: The Best Machine Learning Resources. A curated list of resources for creating your machine learning curriculum.

    By replacing the dominant anthropocentric stance with a universal perspective where living organisms are considered as a special case, long-standing questions in the evaluation of behavior can be addressed in a wider landscape. Can we derive task difficulty intrinsically? Is a universal g factor - a common general component for all abilities - theoretically possible? Using algorithmic information theory as a foundation, the book elaborates on the evaluation of perceptual, developmental, social, verbal and collective features and critically analyzes what the future of intelligence might look like.

    A silent and highly gifted girl chooses to be alone in her attic bedroom. The richest man in the world mysteriously disappears. It all waits to be discovered in THE LONELIEST ROBOT, a brilliant new novel for the modern technological age which features original illustrations from acclaimed robot artist, Matt Dixon. Join a group of unlikely best friends, on a journey of self-discovery as they all transform through life. We can get so lost; we can forget what it's truly like to feel HUMAN. Many things distract us all - smartphones, buying more and more stuff, technology, TV, everyone working longer and harder.... It's so easy to get lost in modern life. An imaginative new book for teens, young adults and anyone with a human heart, which explores and challenges modern life. A thought-provoking dark comedy - this uplifting tale is told with warmth and humour, making it highly digestible for young and curious minds. Also very suitable for adult readers (especially frustrated parents of technology-addicted children!) Discover The Loneliest Robot. Discover yourself! For more, please visit - www.theloneliestrobot.com

    So the biggest question you have is, is the book good and will it be useful for you. Reviews are one way of determining that, but what was a good or bad read for someone else might be different for you. Fortunaltely Amazon makes a free sample available, and I also have a free sample available on my blog "Fairly Nerdy". Both of those samples are approximately 10% of the book which is hopefully enough to help you decide if this is a good book for you. The sample on my blog is the first 10% of the book, I think that Amazon sometimes sends the first 10%, or sometimes sends other sections. You can get the Amazon sample by clicking "Send a free sample" on the right side of this page. You can get the free PDF sample by going to my blog "Fairly Nerdy" and clicking on the "Our Books" tab at the top of the page. (This book is about halfway down on that page) The nice thing about the sample is that it will be a fast read and give you a good high level understanding of Boosting even if you decide you don't want to dig into the details in the rest of the book Several Dozen Visual Examples Equations are great for really understanding every last detail of an algorithm.  But to get a basic idea of how something works, in a way that will stick with you 6 months later, nothing beats pictures.  This book contains several dozen images which detail things such as how a decision tree picks what splits it will make and how they can be combined using boosted learning. Python & Excel Files For The Examples It turns out that Boosting lends itself well to being done iteratively in Excel.  And the nice thing about Excel is that it is easy to follow the equations. And if your spreadsheet can duplicate your code, then you know that you understand the process.  All of the Boosting examples in this book were generated using Python, but then duplicated in Excel, both of which are available for free download. Topics Covered The topics covered in this book are How do decision trees work What are some of the failings of decision trees, and where do they differ from how a human would solve the problem How can multiple decision trees be stacked together into Gradient Boosted Trees How to use the Boosting algorithm to make predictions How is the Boosting algorithm different for regression vs. classification with two categories vs.

     Former mercenary Jake Mudd travels the galaxy transporting goods to out of the way places. He operates under the radar to avoid enemies hellbent on settling old scores. When a lucrative delivery gets hijacked, he goes after the package for the payoff he desperately needs. With the help of his trusty blaster and an alien woman who stirs memories he’s traveled light years to forget, he sets out across a strange and treacherous world. Tracking the cargo turns from difficult to perilous when he falls into the thick of a planetary struggle. With more at stake than his life and the safety of his beloved ship, Jake fights to outrun and outgun the growing threats. But the worst threat of all may be the one he brought with him. Author note: This story takes place just after Tangled Peril and just before Forced Vengeance. However, as with all Jake Mudd Tales, it can be read as a stand-alone without any problem.

    The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of deep learning is widely attributed to a virtuous cycle whereby fundamental advancements in training deeper models were enabled by the availability of massive datasets and high-performance computer hardware.This text serves as a primer for computer architects in a new and rapidly evolving field. We review how machine learning has evolved since its inception in the 1960s and track the key developments leading up to the emergence of the powerful deep learning techniques that emerged in the last decade. Next we review representative workloads, including the most commonly used datasets and seminal networks across a variety of domains. In addition to discussing the workloads themselves, we also detail the most popular deep learning tools and show how aspiring practitioners can use the tools with the workloads to characterize and optimize DNNs.The remainder of the book is dedicated to the design and optimization of hardware and architectures for machine learning. As high-performance hardware was so instrumental in the success of machine learning becoming a practical solution, this chapter recounts a variety of optimizations proposed recently to further improve future designs. Finally, we present a review of recent research published in the area as well as a taxonomy to help readers understand how various contributions fall in context.About the AuthorBrandon Reagen is a Ph.D. candidate at Harvard University. He received his B.S. degree in Computer Systems Engineering and Applied Mathematics from University of Massachusetts, Amherst in 2012 and his M.S. in Computer Science from Harvard in 2014. His research spans the fields of Computer Architecture, VLSI, and Machine Learning with specific interest in designing extremely efficient hardware to enable ubiquitous deployment of Machine Learning models across all compute platforms.Robert Adolf is a Ph.D. candidate in computer architecture at Harvard University. After earning a B.S. in Computer Science from Northwestern University in 2005, he spent four years doing benchmarking and performance analysis of supercomputers at the Department of Defense. In 2009, he joined Pacific Northwest National Laboratory as a research scientist, where he lead a team building large-scale graph analytics on massively multithreaded architectures. His research interests revolve around modeling, analysis, and optimization techniques for high-performance software, with a current focus on deep learning algorithms. His philosophy is that the combination of statistical methods, code analysis, and domain knowledge leads to better tools for understanding and building fast systems.Paul Whatmough leads research on computer architecture for Machine Learning at ARM Research, Boston, MA. He is also an Associate in the School of Engineering and Applied Science at Harvard University. Dr. Whatmough received the B.Eng. degree (with first class Honors) from the University of Lancaster, U.K., M.Sc. degree (with distinction) from the University of Bristol, U.K., and Doctorate degree from University College London, U.K. His research interests span algorithms, computer architecture, and circuits. He has previously led various projects on hardware accelerators, Machine Learning, SoC architecture, Digital Signal Processing (DSP), variation tolerance, and supply voltage noise.Gu-Yeon Wei is Gordon McKay Professor of Electrical Engineering and Computer Science in the School of Engineering and Applied Sciences (SEAS) at Harvard University. He received his B.S., M.S., and Ph.D. degrees in Electrical Engineering from Stanford University in 1994, 1997, and 2001, respectively. His research interests span multiple layers of a computing system: mixed-signal integrated circuits, computer architecture, and design tools for efficient hardware. His research efforts focus on identifying synergistic opportunities across these layers to develop energy-efficient solutions for a broad range of systems from flapping-wing microrobots to machine learning hardware for IoT/edge devices to specialized accelerators for large-scale servers.David Brooks is the Haley Family Professor of Computer Science in the School of Engineering and Applied Sciences at Harvard University. Prior to joining Harvard, he was a research staff member at IBM T. J. Watson Research Center. Prof. Brooks received his B.S. in Electrical Engineering at the University of Southern California and M.A. and Ph.D. degrees in Electrical Engineering at Princeton University. His research interests include resilient and power-efficient computer hardware and software design for high-performance and embedded systems. Prof. Brooks is a Fellow of the IEEE and has received several honors and awards including the ACM Maurice Wilkes Award, ISCA Influential Paper Award, NSF CAREER award, IBM Faculty Partnership Award, and DARPA Young Faculty Award.Margaret Martonosi is the Hugh Trumbull Adams '35 Professor of Computer Science at Princeton University, where she has been on the faculty since 1994. She is also currently serving a four-year term as Director of the Keller Center for Innovation in Engineering Education. Martonosi holds affiliated faculty appointments in Princeton EE, the Center for Information Technology Policy (CITP), the Andlinger Center for Energy and the Environment, and the Princeton Environmental Institute. She also holds an affiliated faculty appointment in Princeton EE. From 2005-2007, she served as Associate Dean for Academic Affairs for the Princeton University School of Engineering and Applied Science. In 2011, she served as Acting Director of Princeton's Center for Information Technology Policy (CITP). From August 2015 through March, 2017, she served as a Jefferson Science Fellow within the U.S. Department of State.Martonosi's research interests are in computer architecture and mobile computing, with particular focus on power-efficient systems. Her work has included the development of the Wattch power modeling tool and the Princeton ZebraNet mobile sensor network project for the design and real-world deployment of zebra tracking collars in Kenya. Her current research focuses on hardware-software interface approaches to manage heterogeneous parallelism and power-performance tradeoffs in systems ranging from smartphones to chip multiprocessors to large-scale data centers.Martonosi is a Fellow of both IEEE and ACM. Notable awards include the 2010 Princeton University Graduate Mentoring Award, the 2013 NCWIT Undergraduate Research Mentoring Award, the 2013 Anita Borg Institute Technical Leadership Award, the 2015 Marie Pistilli Women in EDA Achievement Award, the 2015 ISCA Long-Term Influential Paper Award, and the 2017 ACM SIGMOBILE Test-of-Time Award. In addition to many archival publications, Martonosi is an inventor on seven granted US patents, and has co-authored two technical reference books on power-aware computer architecture. She has served on the Board of Directors of the Computing Research Association (CRA), and will co-chair CRA-W from 2017-2020. Martonosi completed her Ph.D. at Stanford University, and also holds a Master's degree from Stanford and a bachelor's degree from Cornell University, all in Electrical Engineering.

    In the context of artificial intelligence marketing, there are a wide array of predictive analytic techniques available to achieve this purpose, each with its own unique advantages and disadvantages. Artificial Intelligence Marketing and Predicting Consumer Choice serves to integrate these widely disparate approaches, and show the strengths, weaknesses, and best applications of each. It provides a bridge between the person who must apply or learn these problem-solving methods and the community of experts who do the actual analysis. It is also a practical and accessible guide to the many remarkable advances that have been recently made in this fascinating field.Online resources: bonus chapters on AI, ensembles and neural nets, and finishing experiments, plus single and multiple product simulators.

    When the Intergalactic Empire has a tough mission, they send in a wizard scout. When they have an impossible mission, they send in Wizard Scout Trinity Delgado. The planet Cavos is on the verge of civil war. Only an outmanned, outgunned force of Empire peacekeepers prevent an outbreak of bloodshed on a global scale. When the peacekeepers’ commander requests an armored regiment as reinforcements, the Imperial High Command sends him Wizard Scout Trinity instead. With only a fresh out of the university grad student and a half-crazy old pilot as allies, Trinity has to find the source of Cavos’s troubles before the Empire becomes part of a disaster far greater than a mere local civil war. Caught between the peacekeepers’ resentful commander and suspicious local government officials, Trinity and her battle computer, Jennifer, have their work cut out for them. Together they must weave their way through one mystery after another leaving a trail of bloody bodies along the way until they find the answer. That is, if they can remain alive long enough to find it.