CVPR: Computer Vision and Pattern Recognition 2019/2020 Books

CVPR: Computer Vision and Pattern Recognition 2019/2020 Books. The Conference on Computer Vision and Pattern Recognition is an annual conference on computer vision and pattern recognition, which is regarded as one of the most important conferences in its field.

Computer Vision and Pattern Recognition

List NEW RELEASE Books of Computer Vision and Pattern Recognition 2019/2020 you must to Read

1. Python Data Science: How to Learn Step by Step Programming, Data Analytics, and Coding Essentials Tools

In this guidebook, we are going to take some time to explore data science and what it can do for your own business. There are a lot of different tools out there that can help you to get ahead of the competition, and see the results that you want, but none of them are as successful, and backed by concrete data like data science is.

Inside this guidebook, we are actively going to look at what data science is all about, and how well it can work with the Python language. From gathering your data to cleaning it, analyzing it, and picking out the right kind of visualizations, all of the steps and techniques that you need to use will be present right here! We will even take a look at some of the different types of coding that you can do when working with the Python language.

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2. Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow

This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite. Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral. Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies. Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning. Use transfer learning to train models in minutes. Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users

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3. Machine Learning: This book includes Machine Learning for Beginners,Artificial Intelligence and Machine Learning for business, Networking for beginners

Machine Learning is growing exponentially and it’s getting a crucial role both in the business and in the computer networking domain.

Therefore, it’s no longer possible to ignore it and it’s time for you to sit down and understand its practical implications and potentials.

This bundle is a collection of 3 books to help those readers who have no technical background in the field of Machine Learning but want to improve their knowledge of this new amazing technology and its related topics (such as Artificial Intelligence, Deep Learning, Computer Networking).

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4. Mathematics for Machine Learning

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

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5. Machine Learning with Spark and Python: Essential Techniques for Predictive Analytics 2nd Edition

Machine Learning with Spark and Python Essential Techniques for Predictive Analytics, Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves with the addition of Spark―a ML framework from the Apache foundation. By implementing Spark, machine learning students can easily process much large data sets and call the spark algorithms using ordinary Python code.

Machine Learning with Spark and Python focuses on two algorithm families (linear methods and ensemble methods) that effectively predict outcomes. This type of problem covers many use cases such as what ad to place on a web page, predicting prices in securities markets, or detecting credit card fraud. The focus on two families gives enough room for full descriptions of the mechanisms at work in the algorithms. Then the code examples serve to illustrate the workings of the machinery with specific hackable code.

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6. Artificial Intelligence: Learning automation skills with Python (2 books in 1: Artificial Intelligence a modern approach & Artificial Intelligence business applications)

Everything you need to understand and implement Artificial Intelligence!

Learn the potential consequences of Artificial Intelligence and how it will shape the world around us in the coming decades! Become familiar with how Artificial Intelligence aims to aid human cognitive limitations and how it is possible that in the future, the AI that humans create becomes inconceivable to humans themselves. And once you have an understanding of what AI is, you can move forward in your journey to create better informed industry-level business AI applications.

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7. Machine Learning: The Ultimate Beginner’s Guide to Learn Machine Learning, Artificial Intelligence & Neural Networks Step by step

This book will provide the answers you need!Life is becoming ever more complex as we struggle to keep up with technology and use it to our best advantage. It is also more hectic and less certain, even in some of the mundane aspects of our lives, so that we are constantly trying to keep pace. New advancements in technology are paving the way to making life easier for billions and now things like Machine Learning and AI are changing the way we live.In this book, Machine Learning: The Ultimate Beginner’s Guide to Learn Machine Learning, Artificial Intelligence & Neural Networks Step by Step, you will see how this new technology continuously improves itself, can identify trends and patterns with ease and handles a wide variety of data, with chapters that explore::• Teaching the basic principles of Machine Learning• Why it is important and the many benefits that it provides• How Machine Learning differs from conventional programming• The fundamentals of algorithms• Challenges with Machine Learning and how you can easily overcome them• How it is going to change the future and make life easier• And much more…

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8. Feature Extraction and Image Processing for Computer Vision 4th Edition

Feature Extraction for Image Processing and Computer Vision is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in MATLAB and Python. Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated. As one reviewer noted, "The main strength of the proposed book is the link between theory and exemplar code of the algorithms." Essential background theory is carefully explained.

This text gives students and researchers in image processing and computer vision a complete introduction to classic and state-of-the art methods in feature extraction together with practical guidance on their implementation.

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9. Advances in Intelligent Systems and Computing IV

Advances in Intelligent Systems and Computing IV: Selected Papers from the International Conference on Computer Science and Information Technologies, CSIT 2019, September 17-20, 2019, Lviv, Ukraine 1st ed. 2020 Edition. This book reports on new theories and applications in the field of intelligent systems and computing. It covers computational and artificial intelligence methods, as well as advances in computer vision, current issues in big data and cloud computing, computation linguistics, and cyber-physical systems. It also reports on important topics in intelligent information management.

Written by active researchers, the respective chapters are based on selected papers presented at the XIV International Scientific and Technical Conference on Computer Science and Information Technologies (CSIT 2019), held on September 17–20, 2019, in Lviv, Ukraine.

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10. Handbook of Medical Image Computing and Computer Assisted Intervention

Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention.

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11. Spectral Geometry of Shapes: Principles and Applications (Computer Vision and Pattern Recognition)

Spectral Geometry of Shapes presents unique shape analysis approaches based on shape spectrum in differential geometry. It provides insights on how to develop geometry-based methods for 3D shape analysis. The book is an ideal learning resource for graduate students and researchers in computer science, computer engineering and applied mathematics who have an interest in 3D shape analysis, shape motion analysis, image analysis, medical image analysis, computer vision and computer graphics. Due to the rapid advancement of 3D acquisition technologies there has been a big increase in 3D shape data that requires a variety of shape analysis methods, hence the need for this comprehensive resource.

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12. Cause Effect Pairs in Machine Learning (The Springer Series on Challenges in Machine Learning)

This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect (“Does altitude cause a change in atmospheric pressure, or vice versa?”) is here cast as a binary classification problem, to be tackled by machine learning algorithms.  Based on the results of the ChaLearn Cause-Effect Pairs Challenge, this book reveals that the joint distribution of two variables can be scrutinized by machine learning algorithms to reveal the possible existence of a “causal mechanism”, in the sense that the values of one variable may have been generated from the values of the other.
This book provides both tutorial material on the state-of-the-art on cause-effect pairs and exposes the reader to more advanced material, with a collection of selected papers. Supplemental material includes videos, slides, and code which can be found on the workshop website.

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13. Building Machine Learning Powered Applications: Going from Idea to Product

Learn the skills necessary to design, build, and deploy applications powered by machine learning. Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers with little or no ML experience will learn the tools, best practices, and challenges involved in building a real-world ML application step-by-step.

Author Emmanuel Ameisen, who worked as a data scientist at Zipcar and led Insight Data Science’s AI program, demonstrates key ML concepts with code snippets, illustrations, and screenshots from the book’s example application.

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14. Artificial Intelligence: Understanding The Science, Impact, And Future Of A.I, Machine Learning, Neural Networks, And The Singularity

“AI will probably most likely lead to the end of the world, but in the meantime, there'll be great companies.” - Sam Altman
Artificial Intelligence: Understanding The Science, Impact, And Future Of A.I, Machine Learning, Neural Networks, And The SingularityFor the curious mind, there is perhaps no more intriguing a subject than artificial intelligence in the early 21st century. In Artificial Intelligence: Understanding The Science, Impact, And Future Of A.I, Machine Learning, Neural Networks, And The Singularity, author Thien-Nam Dinh draws on his years of experience to tackle the big questions in AI and present them in a way that even a human can understand.

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15. Deep Learning in Object Detection and Recognition

This book discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval.

The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks.

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16. Artificial Intelligence Techniques for Satellite Image Analysis (Remote Sensing and Digital Image Processing)

The main objective of this book is to provide a common platform for diverse concepts in satellite image processing. In particular it presents the state-of-the-art in Artificial Intelligence (AI) methodologies and shares findings that can be translated into real-time applications to benefit humankind. Interdisciplinary in its scope, the book will be of interest to both newcomers and experienced scientists working in the fields of satellite image processing, geo-engineering, remote sensing and Artificial Intelligence. It can be also used as a supplementary textbook for graduate students in various engineering branches related to image processing.

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17. The NeurIPS '18 Competition: From Machine Learning to Intelligent Conversations (The Springer Series on Challenges in Machine Learning)

This volume presents the results of the Neural Information Processing Systems Competition track  at the 2018 NeurIPS conference.  The competition follows the same format as the 2017 competition track for NIPS. Out of 21 submitted proposals, eight competition proposals were selected, spanning the area of Robotics, Health, Computer Vision, Natural Language Processing, Systems and Physics.

Competitions have become an integral part of advancing state-of-the-art in artificial intelligence (AI). They exhibit one important difference to benchmarks: Competitions test a system end-to-end rather than evaluating only a single component; they assess the practicability of an algorithmic solution in addition to assessing feasibility.

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18. Heart of the Machine: Our Future in a World of Artificial Emotional Intelligence

For Readers of Ray Kurzweil and Michio Kaku, a New Look at the Cutting Edge of Artificial Intelligence

Imagine a robotic stuffed animal that can read and respond to a child’s emotional state, a commercial that can recognize and change based on a customer’s facial expression, or a company that can actually create feelings as though a person were experiencing them naturally. Heart of the Machine explores the next giant step in the relationship between humans and technology: the ability of computers to recognize, respond to, and even replicate emotions. Computers have long been integral to our lives, and their advances continue at an exponential rate. Many believe that artificial intelligence equal or superior to human intelligence will happen in the not-too-distance future; some even think machine consciousness will follow. Futurist Richard Yonck argues that emotion, the first, most basic, and most natural form of communication, is at the heart of how we will soon work with and use computers.

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19. Deep Learning: Research and Applications (Frontiers in Computational Intelligence)

This book will focus on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it would provide an insight of deep neural networks in action with illustrative coding examples. Moreover, the book will also provide video demonstrations on each chapter.

Deep learning is a new area of machine learning research, which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non immediately related fields, for example between air pressure recordings and english words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems.

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