Machine learning

Generative modeling is one of the hottest topics in AI. Its now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models.Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, youll understand how to make your models learn more efficiently and become more creative.Discover how variational autoencoders can change facial expressions in photosBuild practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generationCreate recurrent generative models for text generation and learn how to improve the models using attentionUnderstand how generative models can help agents to accomplish tasks within a reinforcement learning settingExplore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

This book offers a comprehensive introduction to the central ideas that underpin deep learning. It is intended both for newcomers to machine learning and for those already experienced in the field. Covering key concepts relating to contemporary architectures and techniques, this essential book equips readers with a robust foundation for potential future specialization. The field of deep learning is undergoing rapid evolution, and therefore this book focusses on ideas that are likely to endure the test of time.The book is organized into numerous bite-sized chapters, each exploring a distinct topic, and the narrative follows a linear progression, with each chapter building upon content from its predecessors. This structure is well-suited to teaching a two-semester undergraduate or postgraduate machine learning course, while remaining equally relevant to those engaged in active research or in self-study.A full understanding of machine learning requires some mathematical background and so the book includes a self-contained introduction to probability theory. However, the focus of the book is on conveying a clear understanding of ideas, with emphasis on the real-world practical value of techniques rather than on abstract theory. Complex concepts are therefore presented from multiple complementary perspectives including textual descriptions, diagrams, mathematical formulae, and pseudo-code.Chris Bishop is a Technical Fellow at Microsoft and is the Director of Microsoft Research AI4Science. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society. Hugh Bishop is an Applied Scientist at Wayve, a deep learning autonomous driving company in London, where he designs and trains deep neural networks. He completed his MPhil in Machine Learning and Machine Intelligence at Cambridge University.¿Chris Bishop wrote a terrific textbook on neural networks in 1995 and has a deep knowledge of the field and its core ideas. His many years of experience in explaining neural networks have made him extremely skillful at presenting complicated ideas in the simplest possible way and it is a delight to see these skills applied to the revolutionary new developments in the field.¿ -- Geoffrey Hinton"With the recent explosion of deep learning and AI as a research topic, and the quickly growing importance of AI applications, a modern textbook on the topic was badly needed. The "New Bishop" masterfully fills the gap, covering algorithms for supervised and unsupervised learning, modern deep learning architecture families, as well as how to apply all of this to various application areas." ¿ Yann LeCun¿This excellent and very educational book will bring the reader up to date with the main concepts and advances in deep learning with a solid anchoring in probability. These concepts are powering current industrial AI systems and are likely to form the basis of further advances towards artificial general intelligence.¿ -- Yoshua Bengio

Between major privacy regulations like the GDPR and CCPA and expensive and notorious data breaches, there has never been so much pressure to ensure data privacy. Unfortunately, integrating privacy into data systems is still complicated. This essential guide will give you a fundamental understanding of modern privacy building blocks, like differential privacy, federated learning, and encrypted computation. Based on hard-won lessons, this book provides solid advice and best practices for integrating breakthrough privacy-enhancing technologies into production systems. Practical Data Privacy answers important questions such as: What do privacy regulations like GDPR and CCPA mean for my data workflows and data science use cases? What does "anonymized data" really mean? How do I actually anonymize data? How does federated learning and analysis work? Homomorphic encryption sounds great, but is it ready for use? How do I compare and choose the best privacy-preserving technologies and methods? Are there open-source libraries that can help? How do I ensure that my data science projects are secure by default and private by design? How do I work with governance and infosec teams to implement internal policies appropriately?

A self-contained and practical introduction that assumes no prior knowledge of programming or machine learning.

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. Table of Contents: Markov Decision Processes / Value Prediction Problems / Control / For Further Exploration

Engineering Deep Learning Systems teaches you to design and implement an automated platform to support creating, training, and maintaining deep learning models. In it, you'll learn just enough about deep learning to understand the needs of the data scientists who will be using your system. You'll learn to gather requirements, translate them into system component design choices, and integrate those components into a cohesive whole. A complete example system and insightful exercises help you build an intuitive understanding of DL system design.

Beg-Int user level

Nowadays, various electronic devices such as digital cameras, smart phones, or even handheld gaming consoles help in obtaining digital photos or shoot short videos. Moreover, images and videos are becoming a part of our daily routine as they remain as a source of occasion and memory of important occurrences in life. For the Human Visual System, the perception of visual feature (i.e., images or videos) can be understood with ease and will throw people back to the happiest moments of life and make them remember someone important said by many psychologists. Usually, roads are systematic in traffic management, yet there is a difficulty in solving problems concerning visually impaired persons. Even normal people get stuck in the traffic and meet with accidents. When it comes to blind people, the issue is even more serious and everyone abuses them as if they are responsible for the accident. It is a common occurrence found on regular roads that rash drivers, despite their own mistakes, always blame others for their wrongdoing.In addition, there is an issue of hiring people with poor eyesight as vehicle drivers, which causes 81% of the accidents on roads. Although this has been happening, no tests are being conducted for drivers regularly in private travel companies. The government officials must ensure eye testing for the drivers regularly. On the other hand, around 26% of accidents involving these drivers may be due to the carelessness of the opposing parties or the vehicle drivers. Nevertheless, when it comes to pedestrians, blind people are often helped by someone to cross the roads but sometimes the blind try it on their own. There is a possibility of an accident occurring in the latter case. This problem has motivated many to think of developing any useful AI interface unit. Face-based gender Recognition (FR) is probably the most dynamic application area that is imaginative and realistic and it has several guidelines to follow at various stages in the process of achieving accuracy. Software-driven automatic Face-based gender Recognition (FR) is probably the most dynamic field in Machine Learning research, attracting numerous proposals in recent years. On a human face, looks can be seen from facial muscle groups and these are sometimes not impacted through inward feeling states.

Over the last decade, the long-running endeavour to automate high-level processes in machine learning (ML) has risen to mainstream prominence. Beyond this, an even loftier goal is the pursuit of autonomy, which describes the capability of the system to independently adjust an ML solution over a lifetime of changing contexts. This monograph provides an expansive perspective on what constitutes an automated/autonomous ML system. In doing so, the authors survey developments in hyperparameter optimisation, multicomponent models, neural architecture search, automated feature engineering, meta-learning, multi-level ensembling, dynamic adaptation, multi-objective evaluation, resource constraints, flexible user involvement, and the principles of generalisation. Furthermore, they develop a conceptual framework throughout to illustrate one possible way of fusing high-level mechanisms into an autonomous ML system. This monograph lays the groundwork for students and researchers to understand the factors limiting architectural integration, without which the field of automated ML risks stifling both its technical advantages and general uptake.

Uncover the secrets of Big Data with our comprehensive book bundle: "Big Data: Statistics, Data Mining, Analytics, and Pattern Learning." Dive into the world of data analytics and processing with Book 1, where you'll gain a solid understanding of the fundamentals necessary to navigate the vast landscape of big data.In Book 2, explore data mining techniques that allow you to extract valuable insights and patterns from large datasets. From marketing to finance and beyond, discover how to uncover hidden trends that drive informed decision-making.Ready to take your skills to the next level? Book 3 delves into advanced data science, where you'll learn to harness the power of machine learning for big data analysis. From regression analysis to neural networks, master the tools and techniques that drive predictive modeling and pattern recognition.Finally, in Book 4, learn how to design robust big data architectures that can scale to meet the needs of modern enterprises. Explore architectural patterns, scalability techniques, and fault tolerance mechanisms that ensure your systems are resilient and reliable.Whether you're a beginner looking to build a solid foundation or an experienced professional seeking to deepen your expertise, this book bundle has something for everyone. Don't miss out on this opportunity to unlock the potential of Big Data and drive innovation in your organization. Order now and embark on your journey to becoming a Big Data expert!

The fusion of the Internet of Things (IoT) with the prowess of Machine Learning and Deep Learning has ignited a revolution in the realm of technology. "IoT Application of Machine Learning and Deep Learning" is a comprehensive journey into this transformative synergy, designed to illuminate the convergence of these cutting-edge domains. This book aims to be a guiding light for enthusiasts, practitioners, and scholars navigating the dynamic landscape where IoT's interconnected devices meet the intelligence of machine and deep learning algorithms.In these pages, readers will embark on an expedition through the myriad applications, breakthroughs, and evolving possibilities of leveraging machine and deep learning within the IoT ecosystem. From unraveling the intricate mechanisms behind predictive analytics in IoT to exploring the robustness of neural networks in enhancing IoT security, this book unveils the intricate tapestry of possibilities awaiting exploration. Whether you're a seasoned professional seeking deeper insights or an eager learner venturing into this interdisciplinary realm, this book endeavors to be a beacon, illuminating the boundless potential at the nexus of IoT, Machine Learning, and Deep Learning.

Welcome to the world of AI and Deep Learning! This book is a culmination of years of research, innovation, and practical insights into the fascinating realms of artificial intelligence and deep learning. In this rapidly evolving landscape, the convergence of neuroscience, mathematics, and computer science has sparked a revolution, redefining how machines perceive, learn, and interact with the world. As you embark on this educational journey, you'll delve into the foundational principles that underpin these technologies, unraveling complex concepts in a lucid and accessible manner. Whether you're an aspiring data scientist, a curious enthusiast, or a seasoned professional seeking to expand your knowledge, this book aims to equip you with a solid understanding of AI and deep learning essentials, empowering you to navigate the frontiers of innovation and contribute meaningfully to this transformative field.In this comprehensive guide, we traverse the landscape of artificial intelligence and deep learning, demystifying intricate theories and methodologies. From the fundamentals of neural networks to the practical applications in image recognition, natural language processing, and beyond, each chapter is meticulously crafted to provide a holistic view of these cutting-edge technologies. With a blend of theoretical foundations, real-world examples, and hands-on exercises, this book is designed to foster a deep comprehension of the core concepts while igniting your creativity to explore and innovate. Join us on this immersive journey, where you'll not only grasp the essence of AI and deep learning but also cultivate the skills to create intelligent systems that can revolutionize industries, redefine possibilities, and shape the future.

This monograph reports the findings of a workshop held at Google (co-organized by Stanford University and the University of Wisconsin-Madison) on the dual-use dilemma posed by GenAI.

Data is important in every part of life. The raw data obtained is to be stored and mined to gain information to use in future. This storing of data is there from long ago where people use to arrange it manually which use to consume more time and not a secured one. With the introduction of computer technology, the data storage is maintained digitally. With the appearance of internet and low-cost hardware storage devices the growth of data gathering and storing has increased to peaks. The data was arranged in structured format with many rows and columns. The different resources of data collection have led organization of data under one site to facilitate data management. Data warehouses are the repositories of data storage which needs to be mined for extracting the patterns and information from the given data. The sensor technology used has generated lots of data beyond the storage in single format and created the urgent need to address the problems of analysis of the collected data for the benefit of future generations [1]. This led to the development of big data and its analytics. Big data in general stores enormous amount of intermixed data which cannot be handled by the traditional methods. Big data which is more in volume, velocity and value is basically needed for the prediction of accurate results [1]. It is more in dimensions by gathering the available information from different sectors. Dimensionality reduction of big data is the needed requirement which needs to be reduced for analyzing the accurate results by dealing with the multiple features of the data objects. Now a day with the rise of the machine learning algorithms [2] high velocity data can also be utilized in an efficient way as machine learning algorithms automatically predict the results without human intervention and they can learn and interpret the results in an effective way apart from any others.

This open access book offers comprehensive, self-contained knowledge on Digital Twin (DT), which is a very promising technology for achieving digital intelligence in the next-generation wireless communications and computing networks. DT is a key technology to connect physical systems and digital spaces in Metaverse.The objectives of this book are to provide the basic concepts of DT, to explore the promising applications of DT integrated with emerging technologies, and to give insights into the possible future directions of DT. For easy understanding, this book also presents several use cases for DT models and applications in different scenarios.The book starts with the basic concepts, models, and network architectures of DT. Then, we present the new opportunities when DT meets edge computing, Blockchain and Artificial Intelligence, anddistributed machine learning (e.g., federated learning, multi-agent deep reinforcement learning). We also present a wide application of DT as an enabling technology for 6G networks, Aerial-Ground Networks, and Unmanned Aerial Vehicles (UAVs).The book allows an easy cross-reference owing to the broad coverage on both the principle and applications of DT. The book is written for people interested in communications and computer networks at all levels. The primary audience includes senior undergraduates, postgraduates, educators, scientists, researchers, developers, engineers, innovators and research strategists.

The mathematical theory of tangles, the centrepiece of the celebrated Robertson-Seymour theory of graph minors, finds precise structure in imprecise data. Assuming only basic undergraduate mathematics, this book shows how tangles can identify, relate, and structure types in data: of behaviour, political views, texts, or proteins.

Solar radiation estimation is an important parameter in engineering applications including solar power plant modelling, photovoltaic cell modelling, and solar heating system modelling. Therefore, the proper estimation of solar radiation is necessary. In recent years, solar radiation prediction models have been established based on parameters including ambient temperature, sunlight period, humidity and cloud coverage estimated from traditional meteorological stations and analyzed indirectly as a function of solar radiation. These models are divided into two categories: artificial intelligence-based parametric methods like Angstrom, and nonparametric methods. It has been found in the literature that data on solar radiation can be calculated using these models at a specific location. One of the easiest ways of measuring solar radiation on the surface is to use sensor data from ground sites, over existing ground points, it also provides high temporal resolution projections of incoming solar radiation. This strategy, however, has a number of technological and financial drawbacks, including high costs and the need for fully skilled labor, as well as the need for daily solar sensor maintenance, washing, and calibration. Ground sensor networks, on the other hand, are hardly ever available insufficient spatial coverage to address spatial pattern. Solar radiation obtained by satellite is a trustworthy instrument to measure solar irradiance at ground level in a wide region. In addition, hourly values obtained were at least as precise as interpolation at a distance of 25 km from ground stations. Multispectral sensors are usually used on satellites to characterize environmental conditions such as light dispersion, reflection and absorption by ray leaves, water vapors, ozone, aerosols and clouds, as the amount of radioactive radiation emitted by the atmosphere not only affects the distribution of the atmospheric components but also the sensitivity of the sensor. The large variety of observation techniques of satellites are thus intended to be perfect for the measurement of spatial variation in solar radiation. Satellite imaging may be utilized in two ways: to design complicated models of radiation transmission utilizing atmospheric characteristics from multi-spectral pictures, or to search for table-based models associated with the radiation process' physical parameterization.

This book constitutes the revised selected papers of the First International Conference, ICCSST 2023, held in Bangalore, India, during May 8¿9, 2023.The 39 full papers included in this volume were carefully reviewed and selected from 200 submissions. They focus on artificial intelligence, blockchain technology, cloud computing, cyber security, data science, e-commerce, computer architecture, image and video processing, pandemic preparedness and digital technology, pattern recognition and classification.

'Generativ AI: 17 vinkler til forståelse og anvendelse' er en lærerig bog, der åbner døren til en spændende verden af generativ kunstig intelligens. Skrevet af to brødre med passion for AI, tager denne bog dig med på en rejse fra teori til praksis, og fra nysgerrighed til forståelse af generativ AI.Med et særligt fokus på populære sprogmodeller som ChatGPT og tekst-til-billede værktøjer, giver bogen dig indblik i, hvordan generativ AI kan påvirke vores måde at tænke, skabe og interagere med teknologi på. Struktureret i 17 uafhængige 'vinkler', dækker bogen et bredt spektrum af emner - fra grundlæggende principper til diverse anvendelsesmetoder.Ud over at give dig en teoretisk forståelse, baseret på den nyeste forskning på området, får du også en praktisk håndbog, der kan tage din brug af AI til et højere niveau. Uanset om du er nybegynder eller erfaren bruger, giver bogen dig værktøjerne og viden til selv at udforske og anvende generativ AI, hvor end du ønsker.Du kan læse mere om bogen på hjemmesiden:https://momentum-ai.dk/bog/

The recent surge of interest in AI systems has raised concerns in moral quarters about their ethical use and whether they can demonstrate fair decision taking processes. Issues of unfairness and discrimination are pervasive when decisions are being made by humans, and are potentially amplified when decisions are made using machines with little transparency, accountability, and fairness. In this monograph, the authors introduce a framework for causal fairness analysis to understand, model, and possibly solve issues of fairness in AI decision-making settings. The authors link the quantification of the disparities present in the observed data with the underlying, often unobserved, collection of causal mechanisms that generate the disparity in the first place, a challenge they call the Fundamental Problem of Causal Fairness Analysis (FPCFA). In order to solve the FPCFA, they study the mapping variations and empirical measures of fairness to structural mechanisms and different units of the population, culminating in the Fairness Map.This monograph presents the first systematic attempt to organize and explain the relationship between various criteria in fairness and studies which causal assumptions are needed for performing causal fairness analysis. The resulting Fairness Cookbook allows anyone to assess the existence of disparate impact and disparate treatment. It is a timely and important introduction to developing future AI systems incorporating inherent fairness and as such will be of wide interest not only to AI system designers, but all who are interested in the wider impact AI will have on society.

"Neural Networks with Python" serves as an introductory guide for those taking their first steps into neural network development with Python. It's tailored to assist beginners in understanding the foundational elements of neural networks and to provide them with the confidence to delve deeper into this intriguing area of machine learning.In this book, readers will embark on a learning journey, starting from the very basics of Python programming, progressing through essential concepts, and gradually building up to more complex neural network architectures. The book simplifies the learning process by using relatable examples and datasets, making the concepts accessible to everyone. You will be introduced to various neural network architectures such as Feedforward, Convolutional, and Recurrent Neural Networks, among others. Each type is explained in a clear and concise manner, with practical examples to illustrate their applications. The book emphasizes the real-world applications and practical aspects of neural network development, rather than just theoretical knowledge.Readers will also find guidance on how to troubleshoot and refine their neural network models. The goal is to equip you with a solid understanding of how to create efficient and effective neural networks, while also being mindful of the common challenges that may arise.By the end of your journey with this book, you will have a foundational understanding of neural networks within the Python ecosystem and be prepared to apply this knowledge to real-world scenarios. "Neural Networks with Python" aims to be your stepping stone into the vast world of machine learning, empowering you to build upon this knowledge and explore more advanced topics in the future.Key LearningsMaster Python for machine learning, from setup to complex models.Gain flexibility with diverse neural network architectures for various problems.Hands-on experience in building, training, and fine-tuning neural networks.Learn strategic approaches for troubleshooting and optimizing neural models.Grasp advanced topics like autoencoders, capsule networks, and attention mechanisms.Acquire skills in crucial data preprocessing and augmentation techniques.Understand and apply optimization techniques and hyperparameter tuning.Implement an end-to-end machine learning project, from data to deployment.Table of ContentPython, TensorFlow, and your First Neural NetworkDeep Dive into Feedforward NetworksConvolutional Networks for Visual TasksRecurrent Networks for Sequence DataData Generation with GANsTransformers for Complex TasksAutoencoders for Data Compression and GenerationCapsule Networks