No specialist knowledge is required to tackle the hands-on exercises using Python and readily available machine learning tools. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Discover powerful machine learning techniques you can understand and apply using only high school math! Put simply, machine learning is a set of techniques for data analysis based on algorithms that deliver better results as you give them more data. ML powers many cutting-edge technologies, such as recommendation systems, facial recognition software, smart speakers, and even self-driving cars. This unique book introduces the core concepts of machine learning, using relatable examples, engaging exercises, and crisp illustrations. About the book Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you’ll build interesting projects with Python, including models for spam detection and image recognition. You’ll also pick up practical skills for cleaning and preparing data. What's inside     Supervised algorithms for classifying and splitting data     Methods for cleaning and simplifying data     Machine learning packages and tools     Neural networks and ensemble methods for complex datasets About the reader For readers who know basic Python. No machine learning knowledge necessary. About the author Luis G. Serrano is a research scientist in quantum artificial intelligence. Previously, he was a Machine Learning Engineer at Google and Lead Artificial Intelligence Educator at Apple. Table of Contents 1 What is machine learning? It is common sense, except done by a computer 2 Types of machine learning 3 Drawing a line close to our points: Linear regression 4 Optimizing the training process: Underfitting, overfitting, testing, and regularization 5 Using lines to split our points: The perceptron algorithm 6 A continuous approach to splitting points: Logistic classifiers 7 How do you measure classification models? Accuracy and its friends 8 Using probability to its maximum: The naive Bayes model 9 Splitting data by asking questions: Decision trees 10 Combining building blocks to gain more power: Neural networks 11 Finding boundaries with style: Support vector machines and the kernel method 12 Combining models to maximize results: Ensemble learning 13 Putting it all in practice: A real-life example of data engineering and machine learning

    Hundreds of thousands of jobs in the field remain unfilled amid breach after breach, and the problem has come to a head. It is time for everyone--not just techies--to become informed and empowered on the subject of cybersecurity.In engaging and exciting fashion, Big Breaches covers some of the largest security breaches and the technical topics behind them such as phishing, malware, third-party compromise, software vulnerabilities, unencrypted data, and more. Cybersecurity affects daily life for all of us, and the area has never been more accessible than with this book.You will obtain a confident grasp on industry insider knowledge such as effective prevention and detection countermeasures, the meta-level causes of breaches, the seven crucial habits for optimal security in your organization, and much more. These valuable lessons are applied to real-world cases, helping you deduce just how high-profile mega-breaches at Target, JPMorgan Chase, Equifax, Marriott, and more were able to occur.Whether you are seeking to implement a stronger foundation of cybersecurity within your organization or you are an individual who wants to learn the basics, Big Breaches ensures that everybody comes away with essential knowledge to move forward successfully. Arm yourself with this book's expert insights and be prepared for the future of cybersecurity. Who This Book Is ForThose interested in understanding what cybersecurity is all about, the failures have taken place in the field to date, and how they could have been avoided. For existing leadership and management in enterprises and government organizations, existing professionals in the field, and for those who are considering entering the field, this book covers everything from how to create a culture of security to the technologies and processes you can employ to achieve security based on lessons that can be learned from past breaches.

    The bit became the universal medium, and the pixel--a particular packaging of bits--conquered the world. Henceforward, nearly every picture in the world would be composed of pixels--cell phone pictures, app interfaces, Mars Rover transmissions, book illustrations, videogames. In A Biography of the Pixel, Pixar cofounder Alvy Ray Smith argues that the pixel is the organizing principle of most modern media, and he presents a few simple but profound ideas that unify the dazzling varieties of digital image making.Smith's story of the pixel's development begins with Fourier waves, proceeds through Turing machines, and ends with the first digital movies from Pixar, DreamWorks, and Blue Sky. Today, almost all the pictures we encounter are digital--mediated by the pixel and irretrievably separated from their media; museums and kindergartens are two of the last outposts of the analog. Smith explains, engagingly and accessibly, how pictures composed of invisible stuff become visible--that is, how digital pixels convert to analog display elements. Taking the special case of digital movies to represent all of Digital Light (his term for pictures constructed of pixels), and drawing on his decades of work in the field, Smith approaches his subject from multiple angles--art, technology, entertainment, business, and history. A Biography of the Pixel is essential reading for anyone who has watched a video on a cell phone, played a videogame, or seen a movie. 400 pages of annotations, prepared by the author and available online, provide an invaluable resource for readers.

    An invaluable educational resource curated by Wild + Free families around the world, this full-color illustrated book offers imaginative suggestions for creating themed book clubs for kids. Here are hands-on activities, games, food, and decoration ideas inspired by a carefully chosen list of beloved classic novels, as well as discussion questions about plots and themes that engage kids minds and sparks their curiosity.Wild + Free Book Club is filled with fun ideas for each book, including:Anne of Green Gables—host a picnic tea partyThe Secret Garden—craft a terrarium, a secret garden of your ownCharlotte’s Web—host an old-time country fairThe Lion the Witch and the Wardrobe—turn your front door into a magical portal to NarniaWith step-by-step instructions, lush photography, and family-tested and kid-approved activities, Wild + Free Book Club will help parents and educators inspire children and instill a lifelong passion for literature and the joy of books.The Wild + Free Book Club reading list:The Adventures of Tom Sawyer Anne of Green Gables Around the World in 80 Days Black Beauty Charlotte’s Web The CrossoverEsperanza RisingThe Evolution of Calpurnia TateFarmer Boy From the Mixed-Up Files of Mrs. Basil E. Frankweiler The Green Ember Heidi The Hobbit Island of the Blue Dolphins The Lion, the Witch and the Wardrobe Little House in the Big Woods A Little PrincessLittle Women Mrs. Frisby and the Rats of NIMH My Side of the Mountain Peter Pan Pippi LongstockingRobin Hood Roll of Thunder, Hear My CryThe Secret GardenThe Swiss Family Robinson Treasure Island The Vanderbeekers of 141st Street

    You’ll work through Python examples to learn key deep learning related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in a neural network, backpropagation, and gradient descent. You’ll also use Python to work through the mathematics that underlies those algorithms and even build a fully-functional neural network.In addition you’ll find coverage of gradient descent including variations commonly used by the deep learning community: SGD, Adam, RMSprop, and Adagrad/Adadelta.