Nowadays bioinformaticians and geneticists are faced with myriad high-throughput data usually presenting the characteristics of uncertainty, high dimensionality and large complexity.
Nowadays bioinformaticians and geneticists are faced with myriad high-throughput data usually presenting the characteristics of uncertainty, high dimensionality and large complexity.
Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications.
It is undeniable that the recent revival of artificial intelligence (AI) has significantly changed the landscape of science in many application domains, ranging from health to defense and from conversational interfaces to autonomous cars.
This book contains select chapters on support vector algorithms from different perspectives, including mathematical background, properties of various kernel functions, and several applications.
This textbook provides an in-depth exploration of statistical learning with reproducing kernels, an active area of research that can shed light on trends associated with deep neural networks.
Federated Learning: A Comprehensive Overview of Methods and Applications presents an in-depth discussion of the most important issues and approaches to federated learning for researchers and practitioners.
This book presents the state-of-the-art applications of machine learning in the finance domain with a focus on financial product modeling, which aims to advance the model performance and minimize risk and uncertainty.
This book explores machine learning (ML) defenses against the many cyberattacks that make our workplaces, schools, private residences, and critical infrastructures vulnerable as a consequence of the dramatic increase in botnets, data ransom, system and network denials of service, sabotage, and data theft attacks.
This book analyses the implications of the technical, legal, ethical and privacy challenges as well as challenges for human rights and civil liberties regarding Artificial Intelligence (AI) and National Security.
Visualize and build deep learning models with 3D data using PyTorch3D and other Python frameworks to conquer real-world application challenges with easeKey FeaturesUnderstand 3D data processing with rendering, PyTorch optimization, and heterogeneous batchingImplement differentiable rendering concepts with practical examplesDiscover how you can ease your work with the latest 3D deep learning techniques using PyTorch3DBook DescriptionWith this hands-on guide to 3D deep learning, developers working with 3D computer vision will be able to put their knowledge to work and get up and running in no time.
Incorporate deep learning into your development projects through hands-on coding and the latest versions of deep learning software, such as TensorFlow 2 and Keras.
This book chronicles a 10-year introduction of blended learning into the delivery at a leading technological university, with a longstanding tradition of technology-enabled teaching and learning, and state-of-the-art infrastructure.
This book discusses computer vision, a noncontact as well as a nondestructive technique involving the development of theoretical and algorithmic tools for automatic visual understanding and recognition which finds huge applications in agricultural productions.
This book constitutes revised and selected papers from the scientific satellite events held in conjunction with the18th International Conference on Service-Oriented Computing, ICSOC 2020.
The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020.
This book addresses one of the most important unsolved problems in artificial intelligence: the task of learning, in an unsupervised manner, from massive quantities of spatiotemporal visual data that are available at low cost.
The contributions gathered in this book focus on modern methods for statistical learning and modeling in data analysis and present a series of engaging real-world applications.
This book presents the proceedings of The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT-2020), held in Shanghai, China, on November 6, 2020.
The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020.
The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020.
This book constitutes the proceedings of the 11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020, as well as two challenges: M&Ms - The Multi-Centre, Multi-Vendor, Multi-Disease Segmentation Challenge, and EMIDEC - Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI Challenge.
Across three volumes, the Handbook of Image Processing and Computer Vision presents a comprehensive review of the full range of topics that comprise the field of computer vision, from the acquisition of signals and formation of images, to learning techniques for scene understanding.
Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice.
A practical, step-by-step guide to using Microsoft's AutoML technology on the Azure Machine Learning service for developers and data scientists working with the Python programming languageKey FeaturesCreate, deploy, productionalize, and scale automated machine learning solutions on Microsoft AzureImprove the accuracy of your ML models through automatic data featurization and model trainingIncrease productivity in your organization by using artificial intelligence to solve common problemsBook DescriptionAutomated Machine Learning with Microsoft Azure will teach you how to build high-performing, accurate machine learning models in record time.
Get to grips with building robust XGBoost models using Python and scikit-learn for deploymentKey FeaturesGet up and running with machine learning and understand how to boost models with XGBoost in no timeBuild real-world machine learning pipelines and fine-tune hyperparameters to achieve optimal resultsDiscover tips and tricks and gain innovative insights from XGBoost Kaggle winnersBook DescriptionXGBoost is an industry-proven, open-source software library that provides a gradient boosting framework for scaling billions of data points quickly and efficiently.
Grasp machine learning concepts, techniques, and algorithms with the help of real-world examples using Python libraries such as TensorFlow and scikit-learnKey FeaturesExploit the power of Python to explore the world of data mining and data analyticsDiscover machine learning algorithms to solve complex challenges faced by data scientists todayUse Python libraries such as TensorFlow and Keras to create smart cognitive actions for your projectsBook DescriptionThe surge in interest in machine learning (ML) is due to the fact that it revolutionizes automation by learning patterns in data and using them to make predictions and decisions.
