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.
Work on practical computer vision projects covering advanced object detector techniques and modern deep learning and machine learning algorithmsKey FeaturesLearn about the new features that help unlock the full potential of OpenCV 4Build face detection applications with a cascade classifier using face landmarksCreate an optical character recognition (OCR) model using deep learning and convolutional neural networksBook DescriptionMastering OpenCV, now in its third edition, targets computer vision engineers taking their first steps toward mastering OpenCV.
Explore Keras, scikit-image, open source computer vision (OpenCV), Matplotlib, and a wide range of other Python tools and frameworks to solve real-world image processing problemsKey FeaturesDiscover solutions to complex image processing tasks using Python tools such as scikit-image and KerasLearn popular concepts such as machine learning, deep learning, and neural networks for image processingExplore common and not-so-common challenges faced in image processingBook DescriptionWith the advancements in wireless devices and mobile technology, there's increasing demand for people with digital image processing skills in order to extract useful information from the ever-growing volume of images.
Develop generative models for a variety of real-world use-cases and deploy them to productionKey FeaturesDiscover various GAN architectures using Python and Keras libraryUnderstand how GAN models function with the help of theoretical and practical examplesApply your learnings to become an active contributor to open source GAN applicationsBook DescriptionGenerative Adversarial Networks (GANs) have revolutionized the fields of machine learning and deep learning.
Discover best practices for choosing, building, training, and improving deep learning models using Keras-R, and TensorFlow-R librariesKey FeaturesImplement deep learning algorithms to build AI models with the help of tips and tricksUnderstand how deep learning models operate using expert techniquesApply reinforcement learning, computer vision, GANs, and NLP using a range of datasetsBook DescriptionDeep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data.
Create advanced applications with Python and OpenCV, exploring the potential of facial recognition, machine learning, deep learning, web computing and augmented reality.
Turn futuristic ideas about computer vision and machine learning into demonstrations that are both functional and entertainingKey FeaturesBuild OpenCV 4 apps with Python 2 and 3 on desktops and Raspberry Pi, Java on Android, and C# in UnityDetect, classify, recognize, and measure real-world objects in real-timeWork with images from diverse sources, including the web, research datasets, and various camerasBook DescriptionOpenCV 4 is a collection of image processing functions and computer vision algorithms.
Bring magic to your mobile apps using TensorFlow Lite and Core MLKey FeaturesExplore machine learning using classification, analytics, and detection tasks.
Quantitative Magnetic Resonance Imaging is a 'go-to' reference for methods and applications of quantitative magnetic resonance imaging, with specific sections on Relaxometry, Perfusion, and Diffusion.
Computing and Visualization for Intravascular Imaging and Computer-Assisted Stenting presents imaging, treatment, and computed assisted technological techniques for diagnostic and intraoperative vascular imaging and stenting.
Computer Vision for Assistive Healthcare describes how advanced computer vision techniques provide tools to support common human needs, such as mental functioning, personal mobility, sensory functions, daily living activities, image processing, pattern recognition, machine learning and how language processing and computer graphics cooperate with robotics to provide such tools.
PDE & Level Sets: Algorithmic Approaches to Static & Motion Imagery is specially dedicated to the segmentation of complex shapes from the field of imaging sciences using level sets and PDEs.
The purpose of Transporting Compressed Digital Video is to introduce fundamental principles and important technologies used in design and analysis of video transport systems for many video applications in digital networks.
Biometrics: Personal Identification in Networked Society is a comprehensive and accessible source of state-of-the-art information on all existing and emerging biometrics: the science of automatically identifying individuals based on their physiological or behavior characteristics.
An exciting new development has taken place in the digital era that has captured the imagination and talent of researchers around the globe - wavelet image compression.
Delivering MPEG-4 Based Audio-Visual Services investigates the different aspects of end-to-end multimedia services; content creation, server and service provider, network, and the end-user terminal.
Master GenAI techniques to create images and text using variational autoencoders (VAEs), generative adversarial networks (GANs), LSTMs, and large language models (LLMs)Key FeaturesImplement real-world applications of LLMs and generative AIFine-tune models with PEFT and LoRA to speed up trainingExpand your LLM toolbox with Retrieval Augmented Generation (RAG) techniques, LangChain, and LlamaIndexPurchase of the print or Kindle book includes a free eBook in PDF formatBook DescriptionBecome an expert in Generative AI through immersive, hands-on projects that leverage today's most powerful models for Natural Language Processing (NLP) and computer vision.
John Maeda is one of the world's preeminent thinkers on technology and design, and in How to Speak Machine, he offers a set of simple laws that govern not only the computers of today, but the unimaginable machines of the future.
The three volume proceedings LNAI 10534 - 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017.
This comprehensive text/reference presents a broad review of diverse domain adaptation (DA) methods for machine learning, with a focus on solutions for visual applications.
