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Deep Learning Research Applications for Natural Language Processing
Humans have the most advanced method of communication, which is known as natural language. While humans can use computers to send voice and text messages to each other, computers do not innately know how to process natural language. In recent years, deep learning has primarily transformed the perspectives of a variety of fields in artificial intelligence (AI), including speech, vision, and natural language processing (NLP). The extensive success of deep learning in a wide variety of applications has served as a benchmark for the many downstream tasks in AI. The field of computer vision has taken great leaps in recent years and surpassed humans in tasks related to detecting and labeling objects thanks to advances in deep learning and neural networks. Deep Learning Research Applications for Natural Language Processing explains the concepts and state-of-the-art research in the fields of NLP, speech, and computer vision. It provides insights into using the tools and libraries in Python for real-world applications. Covering topics such as deep learning algorithms, neural networks, and advanced prediction, this premier reference source is an excellent resource for computational linguists, software engineers, IT managers, computer scientists, students and faculty of higher education, libraries, researchers, and academicians.
R 8,775.56
R 305.00
Machine Learning-Driven Rational Design in Nanomedicine
This book explores how machine learning is transforming nanomedicine, with a focus on the rational design of lipid nanoparticles (LNPs) for mRNA-based therapies. Moving beyond traditional, labor-intensive workflows, it highlights AI-driven methods--such as supervised learning, data augmentation, and deep learning--for predictive modeling and in silico screening. Key topics include chemoinformatics, molecular fingerprinting, and strategies to optimize LNP transfection efficiency and biocompatibility. Real-world applications, including mRNA vaccines and personalized nanomedicines, illustrate the convergence of computational biology and pharmaceutical engineering. It also addresses the ethical considerations and regulatory challenges surrounding AI-driven drug development. This book is intended for researchers, pharmaceutical scientists, computational biologists, and professionals in the biotechnology industry who seek to leverage AI-driven methodologies in nanomedicine development.
R 2,015.00
Deep Learning for Engineers
Deep Learning for Engineers introduces fundamental principles of deep learning along with the explanation of basic elements required for understanding and applying deep learning models.As a comprehensive guideline for applying deep learning models in practical settings, this book features an easy-to-understand coding structure using Python and PyTorch with an in-depth explanation of four typical deep learning case studies on image classification, object detection, semantic segmentation, and image captioning. The fundamentals of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) architectures and their practical implementations in science and engineering are also discussed.This book includes exercise problems for all case studies focusing on various fine-tuning approaches in deep learning. Science and engineering students at both undergraduate and graduate levels, academic researchers, and industry professionals will find the contents useful.
R 2,711.00
R 610.00
Reinforcement Learning
Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve and then gain a clear picture of how they are inter-related. You’ll then work with theories related to reinforcement learning and see the concepts that build up the reinforcement learning process. Reinforcement Learning discusses algorithm implementations important for reinforcement learning, including Markov’s Decision process and Semi Markov Decision process. The next section shows you how to get started with Open AI before looking at Open AI Gym. You’ll then learn about Swarm Intelligence with Python in terms of reinforcement learning. The last part of the book starts with the TensorFlow environment and gives an outline of how reinforcement learning can be applied to TensorFlow. There’s also coverage of Keras, a framework that can be used with reinforcement learning. Finally, you'll delve into Google’s Deep Mind and see scenarios where reinforcement learning can be used. What You'll Learn Absorb the core concepts of the reinforcement learning processUse advanced topics of deep learning and AIWork with Open AI Gym, Open AI, and Python Harness reinforcement learning with TensorFlow and Keras using PythonWho This Book Is ForData scientists, machine learning and deep learning professionals, developers who want to adapt and learn reinforcement learning.
R 1,941.66
Deep Learning and Scientific Computing with R torch
torch is an R port of PyTorch, one of the two most-employed deep learning frameworks in industry and research. It is also an excellent tool to use in scientific computations. It is written entirely in R and C/C++.Though still "young" as a project, R torch already has a vibrant community of users and developers. Experience shows that torch users come from a broad range of different backgrounds. This book aims to be useful to (almost) everyone. Globally speaking, its purposes are threefold:Provide a thorough introduction to torch basics – both by carefully explaining underlying concepts and ideas, and showing enough examples for the reader to become "fluent" in torch Again with a focus on conceptual explanation, show how to use torch in deep-learning applications, ranging from image recognition over time series prediction to audio classificationProvide a concepts-first, reader-friendly introduction to selected scientific-computation topics (namely, matrix computations, the Discrete Fourier Transform, and wavelets), all accompanied by torch code you can play with.Deep Learning and Scientific Computing with R torch is written with first-hand technical expertise and in an engaging, fun-to-read way.
R 9,337.89
Future-Ready with Generative AI
For the first time in history, the most powerful artificial intelligence (AI) works through language itself: The same medium humans use to think and communicate. Dr Michael Kollo reveals why this makes AI uniquely accessible to everyone, and how to harness this linguistic revolution for work and life.This book thoughtfully explores why language-based AI represents something fundamentally different from previous automation, examining both imagined fears and genuine possibilities for workforce transformation. It explains why institutional change takes time despite impressive capabilities, how AI systems are quietly becoming our digital confidants, and why we may eventually come to resent the very technologies we initially embrace. These concepts are explained with care through real-world examples and practical frameworks, whilst companion fictional stories help different audiences understand the emotional reality of technological change. From a software developer orchestrating AI agents to a postal worker finding unexpected digital companionship, these narratives illuminate what it actually feels like to live through this transformation. The book addresses both the remarkable potential of conversational AI and its capacity for subtle manipulation, providing readers with the understanding needed to engage thoughtfully with systems that speak our language.Future-Ready with Generative AI: Skills, Mindsets and Stories in the Age of AI transforms AI from intimidating complexity into intuitive collaboration. Because these systems work through language, everyone can learn to use them effectively. Readers wil gain practical wisdom for leveraging conversational AI while preserving human creativity, relationships, and independent thinking.
R 4,950.00
R 458.00
Machine Learning, Animated
The release of ChatGPT has kicked off an arms race in Machine Learning (ML), however ML has also been described as a black box and very hard to understand. Machine Learning, Animated eases you into basic ML concepts and summarizes the learning process in three words: initialize, adjust and repeat. This is illustrated step by step with animation to show how machines learn: from initial parameter values to adjusting each step, to the final converged parameters and predictions.This book teaches readers to create their own neural networks with dense and convolutional layers, and use them to make binary and multi-category classifications. Readers will learn how to build deep learning game strategies and combine this with reinforcement learning, witnessing AI achieve super-human performance in Atari games such as Breakout, Space Invaders, Seaquest and Beam Rider.Written in a clear and concise style, illustrated with animations and images, this book is particularly appealing to readers with no background in computer science, mathematics or statistics.Access the book's repository at: https://github.com/markhliu/MLA
R 4,066.00
Deep Learning and Data Labeling for Medical Applications
This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions. The 7 papers selected for LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised learning; and modeling of label uncertainty.The 21 papers selected for DLMIA span a wide range of topics such as image description; medical imaging-based diagnosis; medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques.
R 2,173.43
Programming PyTorch for Deep Learning
Take the next steps toward mastering deep learning, the machine learning method that’s transforming the world around us by the second. In this practical book, you’ll get up to speed on key ideas using Facebook’s open source PyTorch framework and gain the latest skills you need to create your very own neural networks.Ian Pointer shows you how to set up PyTorch on a cloud-based environment, then walks you through the creation of neural architectures that facilitate operations on images, sound, text, and more through deep dives into each element. He also covers the critical concepts of applying transfer learning to images, debugging models, and PyTorch in production.Learn how to deploy deep learning models to productionExplore PyTorch use cases from several leading companiesLearn how to apply transfer learning to imagesApply cutting-edge NLP techniques using a model trained on WikipediaUse PyTorch’s torchaudio library to classify audio data with a convolutional-based modelDebug PyTorch models using TensorBoard and flame graphsDeploy PyTorch applications in production in Docker containers and Kubernetes clusters running on Google Cloud
R 1,277.00
Artificial Neural Networks and Machine Learning – ICANN 2024
The ten-volume set LNCS 15016-15025 constitutes the refereed proceedings of the 33rd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2024, held in Lugano, Switzerland, during September 17–20, 2024.The 294 full papers and 16 short papers included in these proceedings were carefully reviewed and selected from 764 submissions. The papers cover the following topics: Part I - theory of neural networks and machine learning; novel methods in machine learning; novel neural architectures; neural architecture search; self-organization; neural processes; novel architectures for computer vision; and fairness in machine learning.Part II - computer vision: classification; computer vision: object detection; computer vision: security and adversarial attacks; computer vision: image enhancement; and computer vision: 3D methods.Part III - computer vision: anomaly detection; computer vision: segmentation; computer vision: pose estimation and tracking; computer vision: video processing; computer vision: generative methods; and topics in computer vision.Part IV - brain-inspired computing; cognitive and computational neuroscience; explainable artificial intelligence; robotics; and reinforcement learning.Part V - graph neural networks; and large language models.Part VI - multimodality; federated learning; and time series processing.Part VII - speech processing; natural language processing; and language modeling.Part VIII - biosignal processing in medicine and physiology; and medical image processing.Part IX - human-computer interfaces; recommender systems; environment and climate; city planning; machine learning in engineering and industry; applications in finance; artificial intelligence in education; social network analysis; artificial intelligence and music; and software security.Part X - workshop: AI in drug discovery; workshop: reservoir computing; special session: accuracy, stability, and robustness in deep neural networks; special session: neurorobotics; and special session: spiking neural networks.
R 2,963.49
Advances in Deep Learning
This book introduces readers to both basic and advanced concepts in deep network models. It covers state-of-the-art deep architectures that many researchers are currently using to overcome the limitations of the traditional artificial neural networks. Various deep architecture models and their components are discussed in detail, and subsequently illustrated by algorithms and selected applications. In addition, the book explains in detail the transfer learning approach for faster training of deep models; the approach is also demonstrated on large volumes of fingerprint and face image datasets. In closing, it discusses the unique set of problems and challenges associated with these models.
R 8,795.47
R 610.00
Llms and Generative AI for Healthcare
Large language models (LLMs) and generative AI are rapidly changing the healthcare industry. These technologies have the potential to revolutionize healthcare by improving the efficiency, accuracy, and personalization of care. This practical book shows healthcare leaders, researchers, data scientists, and AI engineers the potential of LLMs and generative AI today and in the future, using storytelling and illustrative use cases in healthcare. Authors Kerrie Holley and Manish Mathur from Google's Healthcare and Life Sciences Industry team help you explore real-world applications of these technologies in healthcare, from personalized patient care and drug discovery to enhanced medical imaging and robot-assisted surgeries. You'll also learn the challenges of using these technologies--and the ethical implications of their application in this field. With this book, you will: Learn how LLMs and generative AI can help address and transform healthcare issues Explore the basics of LLMs and generative AI and learn how they work Learn how these technologies are being applied in healthcare today Understand several LLM and generative AI use cases Examine the ethics and challenges of applying LLMs and generative AI to healthcare Understand the potential use of LLMs and generative AI in healthcare in the near term and their prospects for the future
R 1,277.00
Feature Engineering for Machine Learning and Data Analytics
Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features.The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively.This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics.
R 2,741.21
Machine and Deep Learning Techniques for Emotion Detection
Computer understanding of human emotions has become crucial and complex within the era of digital interaction and artificial intelligence. Emotion detection, a field within AI, holds promise for enhancing user experiences, personalizing services, and revolutionizing industries. However, navigating this landscape requires a deep understanding of machine and deep learning techniques and the interdisciplinary challenges accompanying them. Machine and Deep Learning Techniques for Emotion Detection offer a comprehensive solution to this pressing problem. Designed for academic scholars, practitioners, and students, it is a guiding light through the intricate terrain of emotion detection. By blending theoretical insights with practical implementations and real-world case studies, our book equips readers with the knowledge and tools needed to advance the frontier of emotion analysis using machine and deep learning methodologies. Focusing on addressing challenges such as cross-cultural variability, data privacy, and model interpretability, Machine and Deep Learning Techniques for Emotion Detection provide a holistic perspective on the ethical, legal, and societal implications of deploying emotion detection technologies. Whether readers are researchers exploring convolutional neural networks for facial expression analysis or practitioners integrating emotion detection into healthcare or marketing, this book provides a comprehensive guide for unlocking the transformative potential of this burgeoning field.
R 8,643.47
Deep Learning with Applications Using Python
Explore deep learning applications, such as computer vision, speech recognition, and chatbots, using frameworks such as TensorFlow and Keras. This book helps you to ramp up your practical know-how in a short period of time and focuses you on the domain, models, and algorithms required for deep learning applications. Deep Learning with Applications Using Python covers topics such as chatbots, natural language processing, and face and object recognition. The goal is to equip you with the concepts, techniques, and algorithm implementations needed to create programs capable of performing deep learning.This book covers convolutional neural networks, recurrent neural networks, and multilayer perceptrons. It also discusses popular APIs such as IBM Watson, Microsoft Azure, and scikit-learn. What You Will Learn Work with various deep learning frameworks such as TensorFlow, Keras, and scikit-learn.Use face recognition and face detection capabilitiesCreate speech-to-text and text-to-speech functionalityEngage with chatbots using deep learningWho This Book Is ForData scientists and developers who want to adapt and build deep learning applications.
R 1,949.89
Deep Learning Techniques and Optimization Strategies in Big Data Analytics
Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.
R 7,942.36
Practical Guide to Integrating Generative AI into HR Management
This book is a practical guide to the integration of generative AI into human resources, covering use cases across recruitment, onboarding, training, and retention. It offers strategic guidance, ethical frameworks, and technical insights to help organisations deploy generative AI effectively while safeguarding fairness, privacy, and compliance. It covers AI-driven applications in talent acquisition, onboarding, employee engagement, career development, and decision-making, while addressing key concerns such as algorithmic bias, data privacy, and regulatory compliance. With practical case studies and deployment templates, the book provides a structured approach to evaluating and integrating generative AI tools within HR systems. From resume screening to personalised learning paths and predictive analytics, this book bridges the gap between AI technologies and HR operations to help organisations build agile, ethical, and employee-centric workplaces.• Examines generative AI’s applications across recruitment, onboarding, L&D, engagement, and retention• Provides strategic frameworks for integrating AI into HR ecosystems• Includes case studies and practical deployment examples from real organizations• Addresses key issues of AI bias, explainability, data privacy, and compliance• Offers guidance on selecting and evaluating HR-specific AI tools• Explains core AI concepts for both technical and non-technical HR professionals• Includes future-focused insights into AI-driven workforce planning and employee experience designThis book is for HR professionals, business leaders, AI developers, and organizational strategists seeking to understand, implement, and manage generative AI solutions in the HR domain.
R 4,066.00
TensorFlow 2 Reinforcement Learning Cookbook
Discover recipes for developing AI applications to solve a variety of real-world business problems using reinforcement learningKey FeaturesDevelop and deploy deep reinforcement learning-based solutions to production pipelines, products, and servicesExplore popular reinforcement learning algorithms such as Q-learning, SARSA, and the actor-critic methodCustomize and build RL-based applications for performing real-world tasksBook DescriptionWith deep reinforcement learning, you can build intelligent agents, products, and services that can go beyond computer vision or perception to perform actions. TensorFlow 2.x is the latest major release of the most popular deep learning framework used to develop and train deep neural networks (DNNs). This book contains easy-to-follow recipes for leveraging TensorFlow 2.x to develop artificial intelligence applications. Starting with an introduction to the fundamentals of deep reinforcement learning and TensorFlow 2.x, the book covers OpenAI Gym, model-based RL, model-free RL, and how to develop basic agents. You'll discover how to implement advanced deep reinforcement learning algorithms such as actor-critic, deep deterministic policy gradients, deep-Q networks, proximal policy optimization, and deep recurrent Q-networks for training your RL agents. As you advance, you’ll explore the applications of reinforcement learning by building cryptocurrency trading agents, stock/share trading agents, and intelligent agents for automating task completion. Finally, you'll find out how to deploy deep reinforcement learning agents to the cloud and build cross-platform apps using TensorFlow 2.x. By the end of this TensorFlow book, you'll have gained a solid understanding of deep reinforcement learning algorithms and their implementations from scratch.What you will learnBuild deep reinforcement learning agents from scratch using the all-new TensorFlow 2.x and Keras APIImplement state-of-the-art deep reinforcement learning algorithms using minimal codeBuild, train, and package deep RL agents for cryptocurrency and stock tradingDeploy RL agents to the cloud and edge to test them by creating desktop, web, and mobile apps and cloud servicesSpeed up agent development using distributed DNN model trainingExplore distributed deep RL architectures and discover opportunities in AIaaS (AI as a Service)Who this book is forThe book is for machine learning application developers, AI and applied AI researchers, data scientists, deep learning practitioners, and students with a basic understanding of reinforcement learning concepts who want to build, train, and deploy their own reinforcement learning systems from scratch using TensorFlow 2.x.
R 1,661.00
R 834.00
Generative AI for Software Development
In just a few short years, AI has transformed software development, and snazzy new tools continue to arrive, with no let-up in sight. How, as a software engineer, product builder, or CTO, do you keep up? This practical book is the result of Sergio Pereira's mission to test every AI tool he could find and provide practitioners with much-needed guidance through the commotion.Generative AI for Software Development focuses on AI tool comparisons, practical workflows, and real-world case studies, with each chapter encompassing critical evaluations of the tools, their use cases, and their limitations. While product reviews are always relevant, the book goes further and delivers to readers a coherent framework for evaluating the tools and workflows of the future, which will continue to complicate the work of software development.Learn how code generation and autocompletion assistants are reshaping the developer experienceDiscover a consistent method for rating code-generation tools based on real-world coding challengesExplore the GenAI tools transforming UI/UX design and frontend developmentLearn how AI is streamlining code reviews and bug detectionReview tools that are simplifying software testing and QAExplore AI for documentation and technical writingUnderstand how modern LLMs have redefined what chatbots can do
R 1,596.00
R 1,527.00
Handbook of Research on Applications and Implementations of Machine Learning Techniques
Artificial intelligence is at the forefront of research and implementation in many industries including healthcare and agriculture. Whether it's detecting disease or generating algorithms, deep learning techniques are advancing exponentially. Researchers and professionals need a platform in which they can keep up with machine learning trends and their developments in the real world.The Handbook of Research on Applications and Implementations of Machine Learning Techniques provides innovative insights into the multi-disciplinary applications of machine learning algorithms for data analytics. The content within this publication examines disease identification, neural networks, and language support. It is designed for IT professionals, developers, data analysts, technology specialists, R&D professionals, industrialists, practitioners, researchers, academicians, and students seeking research on deep learning procedures and their enactments in the fields of medicine, engineering, and computer science.
R 12,562.50
R 1,630.69
Deep Learning for Data Analytics
Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear processing, which aids in feature extraction and learning in supervised and unsupervised ways, including classification and pattern analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning algorithms extract high level complex data and process these complex sets to relatively simpler ideas formulated in the preceding level of the hierarchy. The authors of this book focus on suitable data analytics methods to solve complex real world problems such as medical image recognition, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinoformatics, audio recognition, drug design, and medical image analysis.
R 5,002.00
Understanding the Ethics of Generative AI
The book discusses the ethical complexities that software developers face as they build AI systems capable of autonomous creation. It explores the ethical decisions developers must make when building generative AI systems, from mitigating bias in training data to protecting user privacy and navigating regulatory compliance. Through real-world case studies and actionable frameworks, it equips technical professionals with both the understanding and tools to build AI systems that are fair, transparent, and worthy of public trust.Identifies and corrects algorithmic bias in training datasets, ensuring AI systems produce equitable outputs that don't systematize discriminationDesigns privacy-first AI architectures and implements transparency practices that comply with data protection regulations while building user trustNavigates evolving legal and regulatory landscapes (GDPR, AI Act, sector-specific rules), helping teams stay ahead of compliance requirementsApplies ethical frameworks to real-world decisions: what to do when fairness and accuracy conflict, how to audit AI systems for hidden harms, when to say no to a projectProvides a governance model for embedding ethics into development workflows, not as an afterthought but as core design practice
R 5,165.00
Deep Learning in Multi-step Prediction of Chaotic Dynamics
The book represents the first attempt to systematically deal with the use of deep neural networks to forecast chaotic time series. Differently from most of the current literature, it implements a multi-step approach, i.e., the forecast of an entire interval of future values. This is relevant for many applications, such as model predictive control, that requires predicting the values for the whole receding horizon. Going progressively from deterministic models with different degrees of complexity and chaoticity to noisy systems and then to real-world cases, the book compares the performances of various neural network architectures (feed-forward and recurrent). It also introduces an innovative and powerful approach for training recurrent structures specific for sequence-to-sequence tasks. The book also presents one of the first attempts in the context of environmental time series forecasting of applying transfer-learning techniques such as domain adaptation.
R 2,198.00
Deep Learning in Science
This is the first rigorous, self-contained treatment of the theory of deep learning. Starting with the foundations of the theory and building it up, this is essential reading for any scientists, instructors, and students interested in artificial intelligence and deep learning. It provides guidance on how to think about scientific questions, and leads readers through the history of the field and its fundamental connections to neuroscience. The author discusses many applications to beautiful problems in the natural sciences, in physics, chemistry, and biomedicine. Examples include the search for exotic particles and dark matter in experimental physics, the prediction of molecular properties and reaction outcomes in chemistry, and the prediction of protein structures and the diagnostic analysis of biomedical images in the natural sciences. The text is accompanied by a full set of exercises at different difficulty levels and encourages out-of-the-box thinking.
R 2,630.00
Artificial Neural Networks and Machine Learning – ICANN 2024
The ten-volume set LNCS 15016-15025 constitutes the refereed proceedings of the 33rd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2024, held in Lugano, Switzerland, during September 17–20, 2024.The 294 full papers and 16 short papers included in these proceedings were carefully reviewed and selected from 764 submissions. The papers cover the following topics: Part I - theory of neural networks and machine learning; novel methods in machine learning; novel neural architectures; neural architecture search; self-organization; neural processes; novel architectures for computer vision; and fairness in machine learning.Part II - computer vision: classification; computer vision: object detection; computer vision: security and adversarial attacks; computer vision: image enhancement; and computer vision: 3D methods.Part III - computer vision: anomaly detection; computer vision: segmentation; computer vision: pose estimation and tracking; computer vision: video processing; computer vision: generative methods; and topics in computer vision.Part IV - brain-inspired computing; cognitive and computational neuroscience; explainable artificial intelligence; robotics; and reinforcement learning.Part V - graph neural networks; and large language models.Part VI - multimodality; federated learning; and time series processing.Part VII - speech processing; natural language processing; and language modeling.Part VIII - biosignal processing in medicine and physiology; and medical image processing.Part IX - human-computer interfaces; recommender systems; environment and climate; city planning; machine learning in engineering and industry; applications in finance; artificial intelligence in education; social network analysis; artificial intelligence and music; and software security.Part X - workshop: AI in drug discovery; workshop: reservoir computing; special session: accuracy, stability, and robustness in deep neural networks; special session: neurorobotics; and special session: spiking neural networks.
R 2,969.22
R 306.00
Application of Soft Computing, Machine Learning, Deep Learning and Optimizations in Geoengineering and Geoscience
This book summarizes the application of soft computing techniques, machine learning approaches, deep learning algorithms and optimization techniques in geoengineering including tunnelling, excavation, pipelines, etc. and geoscience including the geohazards, rock and soil properties, etc. The book features state-of-the-art studies on use of SC,ML,DL and optimizations in Geoengineering and Geoscience. Considering these points and understanding, this book will be compiled with highly focussed chapters that will discuss the application of SC,ML,DL and optimizations in Geoengineering and Geoscience. Target audience: (1) Students of UG, PG, and Research Scholars: Several applications of SC,ML,DL and optimizations in Geoengineering and Geoscience can help students to enhance their knowledge in this domain. (2) Industry Personnel and Practitioner: Practitioners from different fields can be able to implement standard and advanced SC,ML,DL and optimizations for solving critical problems of civil engineering.
R 7,421.89
Practical Guide to Quantum Machine Learning and Quantum Optimization
Work with fully explained algorithms and ready-to-use examples that can be run on quantum simulators and actual quantum computers with this comprehensive guideKey FeaturesGet a solid grasp of the principles behind quantum algorithms and optimization with minimal mathematical prerequisitesLearn the process of implementing the algorithms on simulators and actual quantum computersSolve real-world problems using practical examples of methodsBook DescriptionThis book provides deep coverage of modern quantum algorithms that can be used to solve real-world problems. You'll be introduced to quantum computing using a hands-on approach with minimal prerequisites.You'll discover many algorithms, tools, and methods to model optimization problems with the QUBO and Ising formalisms, and you will find out how to solve optimization problems with quantum annealing, QAOA, Grover Adaptive Search (GAS), and VQE. This book also shows you how to train quantum machine learning models, such as quantum support vector machines, quantum neural networks, and quantum generative adversarial networks. The book takes a straightforward path to help you learn about quantum algorithms, illustrating them with code that's ready to be run on quantum simulators and actual quantum computers. You'll also learn how to utilize programming frameworks such as IBM's Qiskit, Xanadu's PennyLane, and D-Wave's Leap.Through reading this book, you will not only build a solid foundation of the fundamentals of quantum computing, but you will also become familiar with a wide variety of modern quantum algorithms. Moreover, this book will give you the programming skills that will enable you to start applying quantum methods to solve practical problems right away.What you will learnReview the basics of quantum computingGain a solid understanding of modern quantum algorithmsUnderstand how to formulate optimization problems with QUBOSolve optimization problems with quantum annealing, QAOA, GAS, and VQEFind out how to create quantum machine learning modelsExplore how quantum support vector machines and quantum neural networks work using Qiskit and PennyLaneDiscover how to implement hybrid architectures using Qiskit and PennyLane and its PyTorch interfaceWho this book is forThis book is for professionals from a wide variety of backgrounds, including computer scientists and programmers, engineers, physicists, chemists, and mathematicians. Basic knowledge of linear algebra and some programming skills (for instance, in Python) are assumed, although all mathematical prerequisites will be covered in the appendices.
R 1,824.00
Generative AI for Students
Using Generative Artificial Intelligence as a student can be confusing and challenging. This authoritative guide equips you with practical knowledge, enabling you to leverage Generative AI as a powerful study aid and embark on your academic journey with confidence. Exploring a wide spectrum of topics, from demystifying jargon and fostering critical thinking to addressing diversity, inclusion and academic integrity, this book is an essential companion for any Higher Education student looking to enhance their academic skill set and achieve success in their studies. By the end of this book you will:- Become critically literate at using Generative AI- Learn how to use Generative AI to enhance traditional study methods- Encounter real-life case studies and worked-through examples to put theory into practice- Gain a comprehensive grounding in how to engage with Generative AI Tools
R 3,438.80
Deep Learning from Scratch
With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. You’ll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way.Author Seth Weidman shows you how neural networks work using a first principles approach. You’ll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, you’ll be set up for success on all future deep learning projects.This book provides:Extremely clear and thorough mental models—accompanied by working code examples and mathematical explanations—for understanding neural networksMethods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented frameworkWorking implementations and clear-cut explanations of convolutional and recurrent neural networksImplementation of these neural network concepts using the popular PyTorch framework
R 1,505.00
R 815.00
Deep Learning in Visual Computing
Deep learning is an artificially intelligent entity that teaches itself and can be utilized to make predictions. Deep learning mimics the human brain and provides learned solutions addressing many challenging problems in the area of visual computing. From object recognition to image classification for diagnostics, deep learning has shown the power of artificial deep neural networks in solving real world visual computing problems with super-human accuracy. The introduction of deep learning into the field of visual computing has meant to be the death of most of the traditional image processing and computer vision techniques. Today, deep learning is considered to be the most powerful, accurate, efficient and effective method with the potential to solve many of the most challenging problems in visual computing.This book provides an insight into deep machine learning and the challenges in visual computing to tackle the novel method of machine learning. It introduces readers to the world of deep neural network architectures with easy-to-understand explanations. From face recognition to image classification for diagnosis of cancer, the book provides unique examples of solved problems in applied visual computing using deep learning. Interested and enthusiastic readers of modern machine learning methods will find this book easy to follow. They will find it a handy guide for designing and implementing their own projects in the field of visual computing.
R 3,407.16
R 1,222.00
Artificial Neural Networks and Machine Learning – ICANN 2024
The ten-volume set LNCS 15016-15025 constitutes the refereed proceedings of the 33rd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2024, held in Lugano, Switzerland, during September 17–20, 2024.The 294 full papers and 16 short papers included in these proceedings were carefully reviewed and selected from 764 submissions. The papers cover the following topics: Part I - theory of neural networks and machine learning; novel methods in machine learning; novel neural architectures; neural architecture search; self-organization; neural processes; novel architectures for computer vision; and fairness in machine learning.Part II - computer vision: classification; computer vision: object detection; computer vision: security and adversarial attacks; computer vision: image enhancement; and computer vision: 3D methods.Part III - computer vision: anomaly detection; computer vision: segmentation; computer vision: pose estimation and tracking; computer vision: video processing; computer vision: generative methods; and topics in computer vision.Part IV - brain-inspired computing; cognitive and computational neuroscience; explainable artificial intelligence; robotics; and reinforcement learning.Part V - graph neural networks; and large language models.Part VI - multimodality; federated learning; and time series processing.Part VII - speech processing; natural language processing; and language modeling.Part VIII - biosignal processing in medicine and physiology; and medical image processing.Part IX - human-computer interfaces; recommender systems; environment and climate; city planning; machine learning in engineering and industry; applications in finance; artificial intelligence in education; social network analysis; artificial intelligence and music; and software security.Part X - workshop: AI in drug discovery; workshop: reservoir computing; special session: accuracy, stability, and robustness in deep neural networks; special session: neurorobotics; and special session: spiking neural networks.
R 2,947.48
