Clustered survival data are encountered in many scientific disciplines including human and veterinary medicine, biology, epidemiology, public health and demography.
Anyone who has ever bought a car, rented an apartment, had a job or conversation that they would rather not see in their employee review may find this book of interest.
One of the challenges for computational intelligence and biometrics is to understand how people process and recognize faces and to develop automated and reliable face recognition systems.
Signal Processing Methods for Music Transcription is the first book dedicated to uniting research related to signal processing algorithms and models for various aspects of music transcription such as pitch analysis, rhythm analysis, percussion transcription, source separation, instrument recognition, and music structure analysis.
Neural networks are members of a class of software that have the potential to enable intelligent computational systems capable of simulating characteristics of biological thinking and learning.
200Ts Vision of Vision One of my formative childhood experiences was in 1968 stepping into the Uptown Theater on Connecticut Avenue in Washington, DC, one of the few movie theaters nationwide that projected in large-screen cinerama.
Although the history of computer-aided face recognition stretches back to the 1960s, automatic face recognition remains an unsolved problem and still offers a great challenge to computer-vision and pattern recognition researchers.
Embedded Image Processing on the TMS320C6000(TM) DSP: Examples in Code Composer Studio(TM) and MATLAB focuses on efficient implementations of advanced image processing algorithms for resource-constrained embedded DSP systems.
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing presents a comprehensive introduction of the use of fuzzy models in pattern recognition and selected topics in image processing and computer vision.
Evolutionary computation is becoming increasingly important for computer vision and pattern recognition and provides a systematic way of synthesis and analysis of object detection and recognition systems.
Privacy, Security and Trust within the Context of Pervasive Computing is an edited volume based on a post workshop at the second international conference on Pervasive Computing.
Imaging for Forensics and Security: From Theory to Practice provides a detailed analysis of new imaging and pattern recognition techniques for the understanding and deployment of biometrics and forensic techniques as practical solutions to increase security.
The human visual system as a functional unit including the eyes, the nervous system, and the corresponding parts of the brain certainly ranks among the most important means of human information processing.
Capturing a wealth of experience about the design of object-oriented software, four top-notch designers present a catalog of simple and succinct solutions to commonly occurring design problems.
The Gang of Four's seminal catalog of 23 patterns to solve commonly occurring design problems Patterns allow designers to create more flexible, elegant, and ultimately reusable designs without having to rediscover the design solutions themselves.
Apply neural network architectures to build state-of-the-art computer vision applications using the Python programming languageKey FeaturesGain a fundamental understanding of advanced computer vision and neural network models in use todayCover tasks such as low-level vision, image classification, and object detectionDevelop deep learning models on cloud platforms and optimize them using TensorFlow Lite and the OpenVINO toolkitBook DescriptionComputer vision allows machines to gain human-level understanding to visualize, process, and analyze images and videos.
Explore self-driving car technology using deep learning and artificial intelligence techniques and libraries such as TensorFlow, Keras, and OpenCVKey FeaturesBuild and train powerful neural network models to build an autonomous carImplement computer vision, deep learning, and AI techniques to create automotive algorithmsOvercome the challenges faced while automating different aspects of driving using modern Python libraries and architecturesBook DescriptionThanks to a number of recent breakthroughs, self-driving car technology is now an emerging subject in the field of artificial intelligence and has shifted data scientists' focus to building autonomous cars that will transform the automotive industry.
Delve into practical computer vision and image processing projects and get up to speed with advanced object detection techniques and machine learning algorithmsKey FeaturesDiscover best practices for engineering and maintaining OpenCV projectsExplore important deep learning tools for image classificationUnderstand basic image matrix formats and filtersBook DescriptionOpenCV is one of the best open source libraries available and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation.
Discover how to integrate KNIME Analytics Platform with deep learning libraries to implement artificial intelligence solutionsKey FeaturesBecome well-versed with KNIME Analytics Platform to perform codeless deep learningDesign and build deep learning workflows quickly and more easily using the KNIME GUIDiscover different deployment options without using a single line of code with KNIME Analytics PlatformBook DescriptionKNIME Analytics Platform is an open source software used to create and design data science workflows.
Explore the potential of deep learning techniques in computer vision applications using the Python ecosystem, and build real-time systems for detecting human behaviorKey FeaturesUnderstand OpenCV and select the right algorithm to solve real-world problemsDiscover techniques for image and video processingLearn how to apply face recognition in videos to automatically extract key informationBook DescriptionComputer Vision (CV) has become an important aspect of AI technology.
A practical guide to learning visual perception for self-driving cars for computer vision and autonomous system engineersKey FeaturesExplore the building blocks of the visual perception system in self-driving carsIdentify objects and lanes to define the boundary of driving surfaces using open-source tools like OpenCV and PythonImprove the object detection and classification capabilities of systems with the help of neural networksBook DescriptionThe visual perception capabilities of a self-driving car are powered by computer vision.
Quickly build and deploy machine learning models without managing infrastructure, and improve productivity using Amazon SageMaker's capabilities such as Amazon SageMaker Studio, Autopilot, Experiments, Debugger, and Model MonitorKey FeaturesBuild, train, and deploy machine learning models quickly using Amazon SageMakerAnalyze, detect, and receive alerts relating to various business problems using machine learning algorithms and techniquesImprove productivity by training and fine-tuning machine learning models in productionBook DescriptionAmazon SageMaker enables you to quickly build, train, and deploy machine learning (ML) models at scale, without managing any infrastructure.
Combine popular machine learning techniques to create ensemble models using PythonKey FeaturesImplement ensemble models using algorithms such as random forests and AdaBoostApply boosting, bagging, and stacking ensemble methods to improve the prediction accuracy of your model Explore real-world data sets and practical examples coded in scikit-learn and KerasBook DescriptionEnsembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power.
Get to grips with traditional computer vision algorithms and deep learning approaches, and build real-world applications with OpenCV and other machine learning frameworksKey FeaturesUnderstand how to capture high-quality image data, detect and track objects, and process the actions of animals or humansImplement your learning in different areas of computer visionExplore advanced concepts in OpenCV such as machine learning, artificial neural network, and augmented realityBook DescriptionOpenCV is a native cross-platform C++ library for computer vision, machine learning, and image processing.
Learn how to build complete machine learning systems with IBM Cloud and Watson Machine learning servicesKey FeaturesImplement data science and machine learning techniques to draw insights from real-world dataUnderstand what IBM Cloud platform can help you to implement cognitive insights within applicationsUnderstand the role of data representation and feature extraction in any machine learning systemBook DescriptionIBM Cloud is a collection of cloud computing services for data analytics using machine learning and artificial intelligence (AI).
Implement popular deep learning techniques to make your IoT applications smarterKey FeaturesUnderstand how deep learning facilitates fast and accurate analytics in IoTBuild intelligent voice and speech recognition apps in TensorFlow and ChainerAnalyze IoT data for making automated decisions and efficient predictionsBook DescriptionArtificial Intelligence is growing quickly, which is driven by advancements in neural networks(NN) and deep learning (DL).
Learn how to deploy effective deep learning solutions on cross-platform applications built using TensorFlow Lite, ML Kit, and FlutterKey FeaturesWork through projects covering mobile vision, style transfer, speech processing, and multimedia processingCover interesting deep learning solutions for mobileBuild your confidence in training models, performance tuning, memory optimization, and neural network deployment through every projectBook DescriptionDeep learning is rapidly becoming the most popular topic in the mobile app industry.
