Feature Extraction and Image Processing for Computer Vision is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in Matlab.
This book provides a careful explanation of the basic areas of electronics and computer architecture, along with lots of examples, to demonstrate the interface, sensor design, programming and microcontroller peripheral setup necessary for embedded systems development.
The exponential explosion of images and videos concerns everybody's common life, since this media is now present everywhere and in all human activities.
Autonomic networking aims to solve the mounting problems created by increasingly complex networks, by enabling devices and service-providers to decide, preferably without human intervention, what to do at any given moment, and ultimately to create self-managing networks that can interface with each other, adapting their behavior to provide the best service to the end-user in all situations.
Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications.
Develop self-learning algorithms and agents using TensorFlow and other Python tools, frameworks, and librariesKey FeaturesLearn, develop, and deploy advanced reinforcement learning algorithms to solve a variety of tasksUnderstand and develop model-free and model-based algorithms for building self-learning agentsWork with advanced Reinforcement Learning concepts and algorithms such as imitation learning and evolution strategiesBook DescriptionReinforcement Learning (RL) is a popular and promising branch of AI that involves making smarter models and agents that can automatically determine ideal behavior based on changing requirements.
Simplify next-generation deep learning by implementing powerful generative models using Python, TensorFlow and KerasKey FeaturesUnderstand the common architecture of different types of GANsTrain, optimize, and deploy GAN applications using TensorFlow and KerasBuild generative models with real-world data sets, including 2D and 3D dataBook DescriptionDeveloping Generative Adversarial Networks (GANs) is a complex task, and it is often hard to find code that is easy to understand.
Build and train scalable neural network models on various platforms by leveraging the power of Caffe2Key FeaturesMigrate models trained with other deep learning frameworks on Caffe2Integrate Caffe2 with Android or iOS and implement deep learning models for mobile devicesLeverage the distributed capabilities of Caffe2 to build models that scale easilyBook DescriptionCaffe2 is a popular deep learning library used for fast and scalable training and inference of deep learning models on various platforms.
Build smarter systems by combining artificial intelligence and the Internet of Things-two of the most talked about topics todayKey FeaturesLeverage the power of Python libraries such as TensorFlow and Keras to work with real-time IoT dataProcess IoT data and predict outcomes in real time to build smart IoT modelsCover practical case studies on industrial IoT, smart cities, and home automationBook DescriptionThere are many applications that use data science and analytics to gain insights from terabytes of data.
Simplify machine learning model implementations with SparkAbout This BookSolve the day-to-day problems of data science with SparkThis unique cookbook consists of exciting and intuitive numerical recipesOptimize your work by acquiring, cleaning, analyzing, predicting, and visualizing your dataWho This Book Is ForThis book is for Scala developers with a fairly good exposure to and understanding of machine learning techniques, but lack practical implementations with Spark.
This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering.
Get a head start in the world of AI and deep learning by developing your skills with PyTorchKey FeaturesLearn how to define your own network architecture in deep learningImplement helpful methods to create and train a model using PyTorch syntaxDiscover how intelligent applications using features like image recognition and speech recognition really process your dataBook DescriptionWant to get to grips with one of the most popular machine learning libraries for deep learning?
Make the best of your test suites by using cutting-edge software architecture patterns in PythonKey FeaturesLearn how to create scalable and maintainable applicationsBuild a web system for micro messaging using concepts in the bookUse profiling to find bottlenecks and improve the speed of the systemBook DescriptionDeveloping large-scale systems that continuously grow in scale and complexity requires a thorough understanding of how software projects should be implemented.
Machine Learning: A Constraint-Based Approach provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that includes neural networks and kernel machines.
This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering.
Neural Systems for Robotics represents the most up-to-date developments in the rapidly growing aplication area of neural networks, which is one of the hottest application areas for neural networks technology.
Theoretical Mechanics of Biological Neural Networks presents an extensive and coherent discusson and formulation of the generation and integration of neuroelectric signals in single neurons.
Artificial Intelligence in Engineering Design is a three volume edited collection of key papers from the field of artificial intelligence and design, aimed at providing a description of the field, and focusing on how ideas and methods from artifical intelligence can help engineers in the design of physical artifacts and processes.
One of the earliest dreams of the fledgling field of artificial intelligence (AI) was to build computer programs that could play games as well as or better than the best human players.
Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations.
Multimodal signal processing is an important research and development field that processes signals and combines information from a variety of modalities - speech, vision, language, text - which significantly enhance the understanding, modelling, and performance of human-computer interaction devices or systems enhancing human-human communication.
Artificial intelligence (AI) is the part of computer science concerned with designing intelligent computer systems (systems that exhibit characteristics we associate with intelligence in human behavior).
Except from the ForewordThe stated aim of the book series "e;Capturing Intelligence"e; is to publish books on research from all disciplines dealing with and affecting the issue of understanding and reproducing intelligence artificial systems.
Handbook of Knowledge Representation describes the essential foundations of Knowledge Representation, which lies at the core of Artificial Intelligence (AI).
Whilst other books cover a broad range of topics, Feature Extraction and Image Processing takes one of the prime targets of applied computer vision, feature extraction, and uses it to provide an essential guide to the implementation of image processing and computer vision techniques.
The first book in this rapidly expanding area, Computer Vision Technology for Food Quality Evaluation thoroughly discusses the latest advances in image processing and analysis.
The Ultimate Tool for MINDSTORMS(R) ManiacsThe new MINDSTORMS kit has been updated to include a programming brick, USB cable, RJ11-like cables, motors, and sensors.
Computational Intelligence: Concepts to Implementations provides the most complete and practical coverage of computational intelligence tools and techniques to date.
This volume covers practical and effective implementation techniques, including recurrent methods, Boltzmann machines, constructive learning with methods for the reduction of complexity in neural network systems, modular systems, associative memory, neural network design based on the concept of the Inductive Logic Unit, and a comprehensive treatment of implementations in the area of data classification.
Image Processing and Pattern Recognition covers major applications in the field, including optical character recognition, speech classification, medical imaging, paper currency recognition, classification reliability techniques, and sensor technology.
New digital image processing and recognition methods, implementation techniques and advanced applications (television, remote sensing, biomedicine, traffic, inspection, robotics, etc.
