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Hello humans & welcome to the world of machines! Specifically, machine learning & algorithms. We are about to embark on an exciting adventure through the vast and varied landscape of algorithms that power the cutting-edge field of artificial intelligence. Machine learning is changing the world as we know it. From predicting stock market trends and diagnosing diseases to powering the virtual assistants in our smartphones and enabling self-driving cars, and picking up the slack on your online dating conversations. What makes this book unique is its structure and depth. With 100 chapters, each dedicated to a different machine learning concept, this book is designed to be your ultimate guide to the world of machine learning algorithms. Whether you are a student, a data science professional, or someone curious about machine learning, this book aims to provide a comprehensive overview that is both accessible and in-depth. The algorithms covered in this book span various categories including: Classification & Regression: Learn about algorithms like Decision Trees, Random Forests, Support Vector Machines, and Logistic Regression which are used to classify data or predict numerical values. Clustering: Discover algorithms like k-Means, Hierarchical Clustering, and DBSCAN that group data points together based on similarities. Neural Networks & Deep Learning: Dive into algorithms and architectures like Perceptrons, Convolutional Neural Networks (CNN), and Long Short-Term Memory Networks (LSTM). Optimization: Understand algorithms like Gradient Descent, Genetic Algorithms, and Particle Swarm Optimization which find the best possible solutions in different scenarios. Ensemble Methods: Explore algorithms like AdaBoost, Gradient Boosting, and Random Forests which combine the predictions of multiple models for improved accuracy. Dimensionality Reduction: Learn about algorithms like Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) which reduce the number of features in a dataset while retaining important information. Reinforcement Learning: Get to know algorithms like Q-learning, Deep Q-Network (DQN), and Monte Carlo Tree Search which are used in systems that learn from their environment. Each chapter is designed as a standalone introduction to its respective algorithm. This means you can start from any chapter that catches your interest or proceed sequentially. Along with the theory, practical examples, applications, and insights into how these algorithms work under the hood are provided. This book is not just an academic endeavor but a bridge that connects theory with practical real-world applications. It's an invitation to explore, learn, and harness the power of algorithms to solve complex problems and make informed decisions. Fasten your seat belts as we dive into the mesmerizing world of machine learning algorithms. Whether you are looking to expand your knowledge, seeking inspiration, or in pursuit of technical mastery, this book should sit on your coffee table and make you look intelligent in front of all invited (and uninvited) guests.
The Hitchhiker's Guide to Python takes the journeyman Pythonista to true expertise. More than any other language, Python was created with the philosophy of simplicity and parsimony. Now 25 years old, Python has become the primary or secondary language (after SQL) for many business users. With popularity comes diversityâ??and possibly dilution. This guide, collaboratively written by over a hundred members of the Python community, describes best practices currently used by package and application developers. Unlike other books for this audience, The Hitchhikerâ??s Guide is light on reusable code and heavier on design philosophy, directing the reader to excellent sources that already exist.
Modern optimization approaches have attracted an increasing number of scientists, decision makers, and researchers. As new issues in this field emerge, different optimization methodologies must be developed and implemented. The Handbook of Research on Emergent Applications of Optimization Algorithms is an authoritative reference source for the latest scholarly research on modern optimization techniques for solving complex problems of global optimization and their applications in economics and engineering. Featuring coverage on a broad range of topics and perspectives such as hybrid systems, non-cooperative games, and cryptography, this publication is ideally designed for students, researchers, and engineers interested in emerging developments in optimization algorithms.
“This book is a remarkable achievement” Gerd Gigerenzer This book offers practical approaches to working in a new field of knowledge - Civic Statistics - which sets out to engage with, and overcome well documented and long-standing problems in teaching quantitative skills. The book includes 23 peer-reviewed chapters, written in coordination by an international group of experts from ten countries. The book aims to support and enhance the work of teachers and lecturers working both at the high school and tertiary (university) levels. It is designed to promote and improve the critical understanding of quantitative evidence relevant to burning social issues – such as epidemics, climate change, poverty, migration, natural disasters, inequality, employment, and racism. Effective citizen engagement with social issues requires active participation and a broad understanding of data and statistics about societal issues. However, many statistics curricula are not designed to teach relevant skills nor to improve learners' statistical literacy. Evidence about social issues is provided to the public via print and digital media, official statistics offices, and other information channels, and a great deal of data is accessible both as aggregated summaries and as individual records. Chapters illustrate the approaches needed to teach and promote the knowledge, skills, dispositions, and enabling processes associated with critical understanding of Civic Statistics presented in many forms. These include: statistical analysis of authentic multivariate data; use of dynamic data visualisations; deconstructing texts about the social and economic well-being of societies and communities. Chapters discuss: the development of curricula and educational resources; use of emerging technologies and visualizations; preparation of teachers and teaching approaches; sources for relevant datasets and rich texts about Civic Statistics; ideas regarding future research, assessment, collaborations between different stakeholders; and other systemic issues.
Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.
The concepts of evolution and complexity theory have become part of the intellectual ether permeating the life sciences, the social and behavioral sciences, and, more recently, management science and economics. In this book, John E. Mayfield elegantly synthesizes core concepts from multiple disciplines to offer a new approach to understanding how evolution works and how complex organisms, structures, organizations, and social orders can and do arise based on information theory and computational science. Intended for the intellectually adventuresome, this book challenges and rewards readers with a nuanced understanding of evolution and complexity that offers consistent, durable, and coherent explanations for major aspects of our life experiences. Numerous examples throughout the book illustrate evolution and complexity formation in action and highlight the core function of computation lying at the work's heart.
This book reflects the author’s years of hands-on experience as an academic and practitioner. It is primarily intended for executives, managers and practitioners who want to redefine the way they think about artificial intelligence (AI) and other exponential technologies. Accordingly the book, which is structured as a collection of largely self-contained articles, includes both general strategic reflections and detailed sector-specific information. More concretely, it shares insights into what it means to work with AI and how to do it more efficiently; what it means to hire a data scientist and what new roles there are in the field; how to use AI in specific industries such as finance or insurance; how AI interacts with other technologies such as blockchain; and, in closing, a review of the use of AI in venture capital, as well as a snapshot of acceleration programs for AI companies.
‘One of the world's sanest, smartest, kindest, funniest voices’ – Independent on Sunday This 42nd Anniversary Edition includes exclusive bonus material from the Douglas Adams archives, and an introduction by Simon Brett, producer of the original radio broadcast. ***** In Life, the Universe and Everything, the third title in Douglas Adams' blockbusting sci-fi comedy series, The Hitchhiker's Guide to the Galaxy, Arthur Dent finds himself enlisted to prevent a galactic war. Following a number of stunning catastrophes, which have involved him being alternately blown up and insulted in ever stranger regions of the Galaxy, Arthur Dent is surprised to find himself living in a cave on prehistoric Earth. However, just as he thinks that things cannot get possibly worse, they suddenly do. An eddy in the space-time continuum lands him, Ford Prefect, and their flying sofa in the middle of the cricket ground at Lord's, just two days before the world is due to be destroyed by the Vogons. Escaping the end of the world for a second time, Arthur, Ford, and their old friend Slartibartfast embark (reluctantly) on a mission to save the whole galaxy from fanatical robots. Not bad for a man in his dressing gown . . . Follow Arthur Dent's galactic (mis)adventures in the rest of the trilogy with five parts: So Long, and Thanks for All the Fish, and Mostly Harmless. ***** Praise for Douglas Adams: 'Sheer delight' - The Times 'A pleasure to read' - New York Times 'Magical . . . read this book' - Sunday Express
Advances in Genetic Programming reports significant results in improving the power of genetic programming, presenting techniques that can be employed immediately in the solution of complex problems in many areas, including machine learning and the simulation of autonomous behavior. Popular languages such as C and C++ are used in manu of the applications and experiments, illustrating how genetic programming is not restricted to symbolic computing languages such as LISP. Researchers interested in getting started in genetic programming will find information on how to begin, on what public-domain code is available, and on how to become part of the active genetic programming community via electronic mail.
Historical and contemporary papers on the philosophical issues raised by the Turing Test as a criterion for intelligence. The Turing Test is part of the vocabulary of popular culture—it has appeared in works ranging from the Broadway play "Breaking the Code" to the comic strip "Robotman." The writings collected by Stuart Shieber for this book examine the profound philosophical issues surrounding the Turing Test as a criterion for intelligence. Alan Turing's idea, originally expressed in a 1950 paper titled "Computing Machinery and Intelligence" and published in the journal Mind, proposed an "indistinguishability test" that compared artifact and person. Following Descartes's dictum that it is the ability to speak that distinguishes human from beast, Turing proposed to test whether machine and person were indistinguishable in regard to verbal ability. He was not, as is often assumed, answering the question "Can machines think?" but proposing a more concrete way to ask it. Turing's proposed thought experiment encapsulates the issues that the writings in The Turing Test define and discuss. The first section of the book contains writings by philosophical precursors, including Descartes, who first proposed the idea of indistinguishablity tests. The second section contains all of Turing's writings on the Turing Test, including not only the Mind paper but also less familiar ephemeral material. The final section opens with responses to Turing's paper published in Mind soon after it first appeared. The bulk of this section, however, consists of papers from a broad spectrum of scholars in the field that directly address the issue of the Turing Test as a test for intelligence. Contributors John R. Searle, Ned Block, Daniel C. Dennett, and Noam Chomsky (in a previously unpublished paper). Each chapter is introduced by background material that can also be read as a self-contained essay on the Turing Test