Machine learning

Master machine learning with SAS Viya!

Elevate your storytelling with SAS Visual AnalyticsData visualization is the gateway to artificial intelligence (AI) and big data.

Elevate your storytelling with SAS Visual AnalyticsData visualization is the gateway to artificial intelligence (AI) and big data.

Machine Learning Governance for Managers provides readers with the knowledge to unlock insights from data and leverage AI solutions.

Building upon the knowledge introduced in The Data Science Framework, this book provides a comprehensive and detailed examination of each aspect of Data Analytics, both from a theoretical and practical standpoint.

This book is the first practical book on AI risk assessment and management.

THIS ISN'T AN EPISODE OF BLACK MIRROR.

This important book introduces the concept of intrusion detection, discusses various approaches for intrusion detection systems (IDS), and presents the architecture and implementation of IDS.

This volume investigates algorithmic methods based on machine learning in order to design sequential investment strategies for financial markets.

Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problemsKey FeaturesDelve into machine learning with this comprehensive guide to scikit-learn and scientific PythonMaster the art of data-driven problem-solving with hands-on examplesFoster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithmsBook DescriptionMachine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners.

An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithmsKey FeaturesExplore statistics and complex mathematics for data-intensive applicationsDiscover new developments in EM algorithm, PCA, and bayesian regressionStudy patterns and make predictions across various datasetsBook DescriptionMachine learning has gained tremendous popularity for its powerful and fast predictions with large datasets.

This book approaches robotics from a deep learning perspective.

A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architecturesKey FeaturesUnderstand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networksLearn the mathematical concepts needed to understand how deep learning models functionUse deep learning for solving problems related to vision, image, text, and sequence applicationsBook DescriptionMost programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts.

This book provides an extensive examination of state-of-the-art methods in multimodal retrieval, generation, and the pioneering field of retrieval-augmented generation.

Build smart applications by implementing real-world artificial intelligence projectsKey FeaturesExplore a variety of AI projects with PythonGet well-versed with different types of neural networks and popular deep learning algorithmsLeverage popular Python deep learning libraries for your AI projectsBook DescriptionArtificial Intelligence (AI) is the newest technology that's being employed among varied businesses, industries, and sectors.

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.

The computer-aided drug design research field comprises several different knowledge areas, and often, researchers are only familiar or experienced with a small fraction of them.

Design and use machine learning models for music generation using Magenta and make them interact with existing music creation toolsKey FeaturesLearn how machine learning, deep learning, and reinforcement learning are used in music generationGenerate new content by manipulating the source data using Magenta utilities, and train machine learning models with itExplore various Magenta projects such as Magenta Studio, MusicVAE, and NSynthBook DescriptionThe importance of machine learning (ML) in art is growing at a rapid pace due to recent advancements in the field, and Magenta is at the forefront of this innovation.

Get hands-on experience in creating state-of-the-art reinforcement learning agents using TensorFlow and RLlib to solve complex real-world business and industry problems with the help of expert tips and best practicesKey FeaturesUnderstand how large-scale state-of-the-art RL algorithms and approaches workApply RL to solve complex problems in marketing, robotics, supply chain, finance, cybersecurity, and moreExplore tips and best practices from experts that will enable you to overcome real-world RL challengesBook DescriptionReinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents.

A remarkable facet of the human brain is its ability to manage multiple tasks with apparent simultaneity.

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.

Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio.

Progress in the application of machine learning (ML) to the physical and life sciences has been rapid.

Progress in the application of machine learning (ML) to the physical and life sciences has been rapid.

This book will help you comprehend the impact of artificial intelligence (AI) on information security, data privacy, and data security.

Increase the performance of various neural network architectures using NEAT, HyperNEAT, ES-HyperNEAT, Novelty Search, SAFE, and deep neuroevolutionKey FeaturesImplement neuroevolution algorithms to improve the performance of neural network architecturesUnderstand evolutionary algorithms and neuroevolution methods with real-world examplesLearn essential neuroevolution concepts and how they are used in domains including games, robotics, and simulationsBook DescriptionNeuroevolution is a form of artificial intelligence learning that uses evolutionary algorithms to simplify the process of solving complex tasks in domains such as games, robotics, and the simulation of natural processes.

Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problemsKey FeaturesUpdated to include new algorithms and techniquesCode updated to Python 3.

Implement various state-of-the-art architectures, such as GANs and autoencoders, for image generation using TensorFlow 2.

Learn how to use R to apply powerful machine learning methods and gain insight into real-world applications using clustering, logistic regressions, random forests, support vector machine, and more.

Implement reinforcement learning techniques and algorithms with the help of real-world examples and recipesKey FeaturesUse PyTorch 1.

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.

Take your machine learning models to the next level by learning how to leverage hyperparameter tuning, allowing you to control the model's finest detailsKey FeaturesGain a deep understanding of how hyperparameter tuning worksExplore exhaustive search, heuristic search, and Bayesian and multi-fidelity optimization methodsLearn which method should be used to solve a specific situation or problemBook DescriptionHyperparameters are an important element in building useful machine learning models.

100 recipes that teach you how to perform various machine learning tasks in the real worldAbout This BookUnderstand which algorithms to use in a given context with the help of this exciting recipe-based guideLearn about perceptrons and see how they are used to build neural networksStuck while making sense of images, text, speech, and real estate?

Think about your data intelligently and ask the right questionsKey FeaturesMaster data cleaning techniques necessary to perform real-world data science and machine learning tasksSpot common problems with dirty data and develop flexible solutions from first principlesTest and refine your newly acquired skills through detailed exercises at the end of each chapterBook DescriptionData cleaning is the all-important first step to successful data science, data analysis, and machine learning.

Comprehensive recipes to give you valuable insights on Transformers, Reinforcement Learning, and moreKey FeaturesDeep Learning solutions from Kaggle Masters and Google Developer ExpertsGet to grips with the fundamentals including variables, matrices, and data sourcesLearn advanced techniques to make your algorithms faster and more accurateBook DescriptionThe independent recipes in Machine Learning Using TensorFlow Cookbook will teach you how to perform complex data computations and gain valuable insights into your data.

This groundbreaking book transcends traditional machine learning approaches by introducing information measurement methodologies that revolutionize the field.

Perform cloud-based machine learning and deep learning using Amazon Web Services such as SageMaker, Lex, Comprehend, Translate, and PollyKey FeaturesExplore popular machine learning and deep learning services with their underlying algorithmsDiscover readily available artificial intelligence(AI) APIs on AWS like Vision and Language ServicesDesign robust architectures to enable experimentation, extensibility, and maintainability of AI appsBook DescriptionFrom data wrangling through to translating text, you can accomplish this and more with the artificial intelligence and machine learning services available on AWS.

A practical guide to mastering reinforcement learning algorithms using KerasKey FeaturesBuild projects across robotics, gaming, and finance fields, putting reinforcement learning (RL) into actionGet to grips with Keras and practice on real-world unstructured datasetsUncover advanced deep learning algorithms such as Monte Carlo, Markov Decision, and Q-learningBook DescriptionReinforcement learning has evolved a lot in the last couple of years and proven to be a successful technique in building smart and intelligent AI networks.

Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering.

Incorporate intelligence to your data-driven business insights and high accuracy business solutionsKey FeaturesExplore IBM Watson capabilities such as Natural Language Processing (NLP) and machine learningBuild projects to adopt IBM Watson across retail, banking, and healthcareLearn forecasting, anomaly detection, and pattern recognition with ML techniquesBook DescriptionIBM Watson provides fast, intelligent insight in ways that the human brain simply can't match.

Understand the fundamentals and develop your own AI solutions in this updated edition packed with many new examplesKey FeaturesAI-based examples to guide you in designing and implementing machine intelligenceBuild machine intelligence from scratch using artificial intelligence examplesDevelop machine intelligence from scratch using real artificial intelligenceBook DescriptionAI has the potential to replicate humans in every field.

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.

Explore various Generative Adversarial Network architectures using the Python ecosystemKey FeaturesUse different datasets to build advanced projects in the Generative Adversarial Network domainImplement projects ranging from generating 3D shapes to a face aging applicationExplore the power of GANs to contribute in open source research and projectsBook DescriptionGenerative Adversarial Networks (GANs) have the potential to build next-generation models, as they can mimic any distribution of data.

Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in PythonKey FeaturesDiscover neural network architectures (like CNN and LSTM) that are driving recent advancements in AIBuild expert neural networks in Python using popular libraries such as KerasIncludes projects such as object detection, face identification, sentiment analysis, and moreBook DescriptionNeural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis, and more.

Skip the theory and get the most out of Tensorflow to build production-ready machine learning modelsKey FeaturesExploit the features of Tensorflow to build and deploy machine learning modelsTrain neural networks to tackle real-world problems in Computer Vision and NLPHandy techniques to write production-ready code for your Tensorflow modelsBook DescriptionTensorFlow is an open source software library for Machine Intelligence.

Learn how to use R to apply powerful machine learning methods and gain insight into real-world applications using clustering, logistic regressions, random forests, support vector machine, and more.

Comprehensive recipes to give you valuable insights on Transformers, Reinforcement Learning, and moreKey FeaturesDeep Learning solutions from Kaggle Masters and Google Developer ExpertsGet to grips with the fundamentals including variables, matrices, and data sourcesLearn advanced techniques to make your algorithms faster and more accurateBook DescriptionThe independent recipes in Machine Learning Using TensorFlow Cookbook will teach you how to perform complex data computations and gain valuable insights into your data.