Deep Code

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English insurance came into being almost entirely during the Elizabethan period. However, the Great Fire of 1666 consumed most of London's mercantile document, and therefore little is known about early English insurance. Using new archival material, this study provides the first in-depth analysis of early English insurance. It focuses on a crucial yet little-known text, the London Insurance Code of the early 1580s, and shows how London insurance customs were first imported from Italy, then influenced by the Dutch, and finally shaped in a systematic fashion in that Insurance Code. The London Insurance Code was in turn heavily influenced by coeval continental codes. This deep influence attests the strong links between English and European insurance, and questions the common/civil law divide on the history of commercial law.

Today, most developers use object-oriented languages. However, objects offers far more power than typical developers ever access: power that's only accessible to those with a deep understanding of the

From spy missions to code breaking, this richly illustrated account of the covert operations of World War II takes readers behind the battle lines and deep into the undercover war effort that changed

"Some books are deeper and more profound than others.They touch us at a deep soul level.This is one of those books.Your Owner's Manual for Life epitomizes the Truth that our greatest res

A Latina teen spy goes undercover as a white girl to stop a white supremacist terrorist plot in a fast-paced middle-grade debut from a seasoned author of contemporary crime fiction.In her debut for younger readers, Aya de León pits a teen spy against the ominous workings of a white nationalist. Fourteen-year-old Andréa Hernández-Baldoquín hails from a family of spies working for the Factory, an international organization dedicated to protecting people of color. For her first solo mission, Andréa straightens her hair and goes undercover as Andrea Burke, a white girl, to befriend the estranged son of a dangerous white supremacist. In addition to her Factory training, the assignment calls for a deep dive into the son’s interests―comic books and gaming―all while taking care not to speak Spanish and blow her family’s cover. But it’s hard to hide who you really are, especially when you develop a crush on your target’s Latino best friend. Can Andréa keep her head, her geek cred, and her code-

As a pediatrician, Katrina Ubell, MD, always struggled with her weight--she was either 40 pounds overweight, or struggling to lose that weight. Although she'd regularly counsel parents on the importance of keeping their kids healthy and fostering good eating habits, Dr. Ubell, as a busy professional, was never able to do the same for herself. Like everyone else, she tried many different diets and programs, but would always regain the weight. In 2015, Dr. Ubell "cracked the code" for making weight loss permanent, and developed a program targeted at busy physicians like herself who often de-prioritize their own wellness. As a weight loss coach, Dr. Ubell has helped over 1,000 busy physicians and professionals find and stay at a healthy weight with her brain-based program, and is now ready to bring this program to the general public. Dr. Ubell's program doesn't involve any unrealistic diets, plans, special foods, supplements, or even rigorous exercise protocols; instead, she uses a deep u

A high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of 'deep' or 'Q', or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra. A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails. Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning. Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control. These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning.

Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design mod

Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. Readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. Applications presented include scientific imaging, communication, face recognition, 3D vision, and deep networks for classification. With code available online, this is an ideal textbook for senior and graduate students in computer science, data science, and electrical engineering, as well as for those taking courses on sparsity, low-dimensional str

Accurate, robust and fast image reconstruction is a critical task in many scientific, industrial and medical applications. Over the last decade, image reconstruction has been revolutionized by the rise of compressive imaging. It has fundamentally changed the way modern image reconstruction is performed. This in-depth treatment of the subject commences with a practical introduction to compressive imaging, supplemented with examples and downloadable code, intended for readers without extensive background in the subject. Next, it introduces core topics in compressive imaging – including compressed sensing, wavelets and optimization – in a concise yet rigorous way, before providing a detailed treatment of the mathematics of compressive imaging. The final part is devoted to recent trends in compressive imaging: deep learning and neural networks. With an eye to the next decade of imaging research, and using both empirical and mathematical insights, it examines the potential benefits and the

A unique text integrating numerics, mathematics and applications to provide a hands-on approach to using optimization techniques, this mathematically accessible textbook emphasises conceptual understanding and importance of theorems rather than elaborate proofs. It allows students to develop fundamental optimization methods before delving into MATLAB®'s optimization toolbox, and to link MATLAB's results with the results from their own code. Following a practical approach, the text demonstrates several applications, from error-free analytic examples to truss (size) optimization, and 2D and 3D shape optimization, where numerical errors are inevitable. The principle of minimum potential energy is discussed to highlight the deep relationship between engineering and optimization. MATLAB code in every chapter illustrates key concepts and the text demonstrates the coupling between MATLAB and SOLIDWORKS® for design optimization. A wide variety of optimization problems are covered including con

With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature, including classification, sequence labelling, and sequence-to-sequence problems. Topics covered include statistical machine learning and deep learning models, text classification and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based methods. Rich connections are drawn between concepts throughout the book, equipping students with the tools needed to establish a deep understanding of NLP solutions, adapt existing models, and confidently develop innovative models of their own. Featuring a host of examples, intuition, and end of chapter exercises, plus sample code available as an onli

Deep learning is revolutionizing how machine translation systems are built today. This book introduces the challenge of machine translation and evaluation - including historical, linguistic, and applied context -- then develops the core deep learning methods used for natural language applications. Code examples in Python give readers a hands-on blueprint for understanding and implementing their own machine translation systems. The book also provides extensive coverage of machine learning tricks, issues involved in handling various forms of data, model enhancements, and current challenges and methods for analysis and visualization. Summaries of the current research in the field make this a state-of-the-art textbook for undergraduate and graduate classes, as well as an essential reference for researchers and developers interested in other applications of neural methods in the broader field of human language processing.

Finite-state methods are the most efficient mechanisms for analysing textual and symbolic data, providing elegant solutions for an immense number of practical problems in computational linguistics and computer science. This book for graduate students and researchers gives a complete coverage of the field, starting from a conceptual introduction and building to advanced topics and applications. The central finite-state technologies are introduced with mathematical rigour, ranging from simple finite-state automata to transducers and bimachines as 'input-output' devices. Special attention is given to the rich possibilities of simplifying, transforming and combining finite-state devices. All algorithms presented are accompanied by full correctness proofs and executable source code in a new programming language, C(M), which focuses on transparency of steps and simplicity of code. Thus, by enabling readers to obtain a deep formal understanding of the subject and to put finite-state methods t

Along with updating all chapters and Python code examples, the second edition of this bestseller includes new chapters on Gaussian processes, Boltzmann machines, and deep belief networks. It also revi

One of China’s bestselling novels, an unusual literary thriller that takes us deep into the world of code breakingIn his gripping debut novel, Mai Jia reveals the mysterious world of Unit 701, a top-s

This book, first published in 2000, explores the exciting field of complexity. It features in-depth coverage of important theoretical areas, including fractals, chaos, non-linear dynamics, artificial life and self-organization. It also provides overviews of complexity in several applied areas, including parallel computation, control systems, neural systems and ecosystems. Some of the properties that best characterize complex systems, including algorithmic richness, non-linearity and abundant interactions between components are examined. In this way the book draws out themes, especially the ideas of connectivity and natural computation, that reveal deep, underlying similarities between phenomena that have formerly been treated as completely distinct. The idea of natural computation is particularly rich in fresh approaches applicable to both biology and computing. Analogies such as the DNA code as life's underlying program, or organisms as automata, are very compelling. Conversely, biolo

If you want to ensure a bright future for your child thank to coding, then keep reading...Hey you, father or mother that are reading these lines, let me introduce myself. I'm Raymond Deep, expert programmer since 1999 and father of two wonderful kids.Do you know that the median annual wage for computer programmers was $86,550 in 2019? I want, like you, give a chance to my childs and, you know, coding is the skill that each one wants on the market.Children want to have fun and I was looking for, without ever being satisfied, a product that would explain step by step, without the intervention of a parent and in a funny way, how to start writing the first line of code.Searching on internet, I discovered an image-based programming language that is perfect for kids to learn the logic of programming, how a programmer has to think: SCRATCH 3.This language/tool is fantastic and, because I'm an experienced programmer, I saw the big potential and I started to study all details and features of th

The award-winning author of The Music of the Primes explores the future of creativity and how machine learning will disrupt, enrich, and transform our understanding of what it means to be human.Can a well-programmed machine do anything a human can—only better? Complex algorithms are buying our groceries, picking our partners, and driving our investments. They can navigate more data than a doctor or lawyer and act with greater precision. For many years we’ve taken solace in the notion that they can’t create. But now that algorithms can learn and adapt, does the future of creativity belong to machines too?It is hard to imagine a better guide to the bewildering world of artificial intelligence than Marcus du Sautoy, a celebrated Oxford mathematician whose work on symmetry in the ninth dimension has taken him to the vertiginous edge of mathematical understanding. In The Creativity Code he considers what machine learning means for the future of creativity. Programs like Deep Dream produce d

Deep beneath the city live five extraordinary kids: world-famous hacker Jack, gadget geek Charlie, free runner Slink, communications chief Obi, and decoy expert Wren. Orphans bonded over their shared sense of justice, the kids have formed the Urban Outlaws, a group dedicated to outsmarting criminals and handing out their stolen money through Random Acts of Kindness (R.A.K.s).But the kids find themselves in serious trouble when they're caught in an epic battle to control Proteus, a genius super-computer. Proteus can crack any code in the world—and steal top-secret documents in nanoseconds. It's down to the Urban Outlaws to use their guile, guts, and skill to destroy the computer, avert world domination . . . and stay alive.Don't miss the global launch of this debut series from Peter Jay Black—the perfect mix of explosive action, cool technology, and characters with lots of heart.