Download or read book Deep Learning for the Life Sciences written by Bharath Ramsundar and published by O'Reilly Media. This book was released on 2019-04-10 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You’ll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine—an example that represents one of science’s greatest challenges. Learn the basics of performing machine learning on molecular data Understand why deep learning is a powerful tool for genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use deep learning to analyze microscopic images Analyze medical scans using deep learning techniques Learn about variational autoencoders and generative adversarial networks Interpret what your model is doing and how it’s working

Download or read book Machine Learning in Biotechnology and Life Sciences written by Saleh Alkhalifa and published by Packt Publishing Ltd. This book was released on 2022-01-28 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore all the tools and templates needed for data scientists to drive success in their biotechnology careers with this comprehensive guide Key FeaturesLearn the applications of machine learning in biotechnology and life science sectorsDiscover exciting real-world applications of deep learning and natural language processingUnderstand the general process of deploying models to cloud platforms such as AWS and GCPBook Description The booming fields of biotechnology and life sciences have seen drastic changes over the last few years. With competition growing in every corner, companies around the globe are looking to data-driven methods such as machine learning to optimize processes and reduce costs. This book helps lab scientists, engineers, and managers to develop a data scientist's mindset by taking a hands-on approach to learning about the applications of machine learning to increase productivity and efficiency in no time. You'll start with a crash course in Python, SQL, and data science to develop and tune sophisticated models from scratch to automate processes and make predictions in the biotechnology and life sciences domain. As you advance, the book covers a number of advanced techniques in machine learning, deep learning, and natural language processing using real-world data. By the end of this machine learning book, you'll be able to build and deploy your own machine learning models to automate processes and make predictions using AWS and GCP. What you will learnGet started with Python programming and Structured Query Language (SQL)Develop a machine learning predictive model from scratch using PythonFine-tune deep learning models to optimize their performance for various tasksFind out how to deploy, evaluate, and monitor a model in the cloudUnderstand how to apply advanced techniques to real-world dataDiscover how to use key deep learning methods such as LSTMs and transformersWho this book is for This book is for data scientists and scientific professionals looking to transcend to the biotechnology domain. Scientific professionals who are already established within the pharmaceutical and biotechnology sectors will find this book useful. A basic understanding of Python programming and beginner-level background in data science conjunction is needed to get the most out of this book.

Download or read book Principles and Labs for Deep Learning written by Shih-Chia Huang and published by Academic Press. This book was released on 2021-07-06 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: Principles and Labs for Deep Learning provides the knowledge and techniques needed to help readers design and develop deep learning models. Deep Learning techniques are introduced through theory, comprehensively illustrated, explained through the TensorFlow source code examples, and analyzed through the visualization of results. The structured methods and labs provided by Dr. Huang and Dr. Le enable readers to become proficient in TensorFlow to build deep Convolutional Neural Networks (CNNs) through custom APIs, high-level Keras APIs, Keras Applications, and TensorFlow Hub. Each chapter has one corresponding Lab with step-by-step instruction to help the reader practice and accomplish a specific learning outcome. Deep Learning has been successfully applied in diverse fields such as computer vision, audio processing, robotics, natural language processing, bioinformatics and chemistry. Because of the huge scope of knowledge in Deep Learning, a lot of time is required to understand and deploy useful, working applications, hence the importance of this new resource. Both theory lessons and experiments are included in each chapter to introduce the techniques and provide source code examples to practice using them. All Labs for this book are placed on GitHub to facilitate the download. The book is written based on the assumption that the reader knows basic Python for programming and basic Machine Learning. Introduces readers to the usefulness of neural networks and Deep Learning methods Provides readers with in-depth understanding of the architecture and operation of Deep Convolutional Neural Networks Demonstrates the visualization needed for designing neural networks Provides readers with an in-depth understanding of regression problems, binary classification problems, multi-category classification problems, Variational Auto-Encoder, Generative Adversarial Network, and Object detection

Download or read book Deep Learning in Biology and Medicine written by Davide Bacciu and published by World Scientific Publishing Europe Limited. This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biology, medicine and biochemistry have become data-centric fields for which Deep Learning methods are delivering groundbreaking results. Addressing high impact challenges, Deep Learning in Biology and Medicine provides an accessible and organic collection of Deep Learning essays on bioinformatics and medicine. It caters for a wide readership, ranging from machine learning practitioners and data scientists seeking methodological knowledge to address biomedical applications, to life science specialists in search of a gentle reference for advanced data analytics.With contributions from internationally renowned experts, the book covers foundational methodologies in a wide spectrum of life sciences applications, including electronic health record processing, diagnostic imaging, text processing, as well as omics-data processing. This survey of consolidated problems is complemented by a selection of advanced applications, including cheminformatics and biomedical interaction network analysis. A modern and mindful approach to the use of data-driven methodologies in the life sciences also requires careful consideration of the associated societal, ethical, legal and transparency challenges, which are covered in the concluding chapters of this book.

Download or read book Deep Learning for the Earth Sciences written by Gustau Camps-Valls and published by John Wiley & Sons. This book was released on 2021-08-18 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.

Download or read book Data Analysis for the Life Sciences with R written by Rafael A. Irizarry and published by CRC Press. This book was released on 2016-10-04 with total page 537 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained.

Download or read book Demystifying Big Data Machine Learning and Deep Learning for Healthcare Analytics written by Pradeep N and published by Academic Press. This book was released on 2021-06-10 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the impact of these techniques on healthcare analytics. The book is divided into two parts: Part 1 covers big data aspects such as healthcare decision support systems and analytics-related topics. Part 2 focuses on the current frameworks and applications of deep learning and machine learning, and provides an outlook on future directions of research and development. The entire book takes a case study approach, providing a wealth of real-world case studies in the application chapters to act as a foundational reference for biomedical engineers, computer scientists, healthcare researchers, and clinicians. Provides a comprehensive reference for biomedical engineers, computer scientists, advanced industry practitioners, researchers, and clinicians to understand and develop healthcare analytics using advanced tools and technologies Includes in-depth illustrations of advanced techniques via dataset samples, statistical tables, and graphs with algorithms and computational methods for developing new applications in healthcare informatics Unique case study approach provides readers with insights for practical clinical implementation

Download or read book Machine Learning and Deep Learning in Real Time Applications written by Mahrishi, Mehul and published by IGI Global. This book was released on 2020-04-24 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence and its various components are rapidly engulfing almost every professional industry. Specific features of AI that have proven to be vital solutions to numerous real-world issues are machine learning and deep learning. These intelligent agents unlock higher levels of performance and efficiency, creating a wide span of industrial applications. However, there is a lack of research on the specific uses of machine/deep learning in the professional realm. Machine Learning and Deep Learning in Real-Time Applications provides emerging research exploring the theoretical and practical aspects of machine learning and deep learning and their implementations as well as their ability to solve real-world problems within several professional disciplines including healthcare, business, and computer science. Featuring coverage on a broad range of topics such as image processing, medical improvements, and smart grids, this book is ideally designed for researchers, academicians, scientists, industry experts, scholars, IT professionals, engineers, and students seeking current research on the multifaceted uses and implementations of machine learning and deep learning across the globe.

Download or read book Deep Learning written by Siddhartha Bhattacharyya and published by Walter de Gruyter GmbH & Co KG. This book was released on 2020-06-22 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep learning is a new area of machine learning research which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non-immediately related fields, for example between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g. classification) and/or unsupervised (e.g. pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition.

Download or read book Machine Learning for Planetary Science written by Joern Helbert and published by Elsevier. This book was released on 2022-03-22 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific understanding of planetary data and serves as a guide for selecting the right methods and tools for solving a variety of everyday problems in planetary science using machine learning. Illustrating ways to employ machine learning in practice with case studies, the book is clearly organized into four parts to provide thorough context and easy navigation. The book covers a range of issues, from data analysis on the ground to data analysis onboard a spacecraft, and from prioritization of novel or interesting observations to enhanced missions planning. This book is therefore a key resource for planetary scientists working in data analysis, missions planning, and scientific observation. Includes links to a code repository for sharing codes and examples, some of which include executable Jupyter notebook files that can serve as tutorials Presents methods applicable to everyday problems faced by planetary scientists and sufficient for analyzing large datasets Serves as a guide for selecting the right method and tools for applying machine learning to particular analysis problems Utilizes case studies to illustrate how machine learning methods can be employed in practice

Download or read book Machine Learning A Journey To Deep Learning With Exercises And Answers written by Andreas Miroslaus Wichert and published by World Scientific. This book was released on 2021-01-26 with total page 641 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique compendium discusses some core ideas for the development and implementation of machine learning from three different perspectives — the statistical perspective, the artificial neural network perspective and the deep learning methodology.The useful reference text represents a solid foundation in machine learning and should prepare readers to apply and understand machine learning algorithms as well as to invent new machine learning methods. It tells a story outgoing from a perceptron to deep learning highlighted with concrete examples, including exercises and answers for the students.Related Link(s)

Download or read book Deep Learning For Physics Research written by Martin Erdmann and published by World Scientific. This book was released on 2021-06-25 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: A core principle of physics is knowledge gained from data. Thus, deep learning has instantly entered physics and may become a new paradigm in basic and applied research.This textbook addresses physics students and physicists who want to understand what deep learning actually means, and what is the potential for their own scientific projects. Being familiar with linear algebra and parameter optimization is sufficient to jump-start deep learning. Adopting a pragmatic approach, basic and advanced applications in physics research are described. Also offered are simple hands-on exercises for implementing deep networks for which python code and training data can be downloaded.

Download or read book Handbook of Research on New Investigations in Artificial Life AI and Machine Learning written by Maki K. Habib and published by Engineering Science Reference. This book was released on 2022 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides the latest research, investigation, and development in the area of living systems intelligence, human-level cognition & artificial systems, nature and bioinspiration, machine learning techniques, Deep Learning techniques and applications, and systems that exhibit intelligent autonomous behavioral characteristics"--

Download or read book Deep Learning for Medical Applications with Unique Data written by Deepak Gupta and published by Academic Press. This book was released on 2022-02-15 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning for Medical Applications with Unique Data informs readers about the most recent deep learning-based medical applications in which only unique data gathered in real cases are used. The book provides examples of how deep learning can be used in different problem areas and frameworks in both clinical and research settings, including medical image analysis, medical image registration, time series analysis, medical data synthesis, drug discovery, and pre-processing operations. The volume discusses not only positive findings, but also negative ones obtained by deep learning techniques, including the use of newly developed deep learning techniques rarely reported in the existing literature. The book excludes research works with ready data sets and includes only unique data use to better understand the state of deep learning in real-world cases, along with the feedback and user experiences from physicians and medical staff for applied deep learning-based solutions. Other applications presented in the book include hybrid solutions with deep learning support, disease diagnosis with deep learning focusing on rare diseases and cancer, patient care and treatment, genomics research, as well as research on robotics and autonomous systems. Introduces deep learning, demonstrating concepts for a wide variety of medical applications using unique data, excluding research with ready datasets Encompasses a wide variety of biomedical applications, including unsupervised learning, natural language processing, pattern recognition, image and video processing and disease diagnosis Provides a robust set of methods that will help readers appropriately and judiciously use the most suitable deep learning techniques for their applications

Download or read book Real World AI written by Alyssa Simpson Rochwerger and published by Lioncrest Publishing. This book was released on 2021-03-16 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: How can you successfully deploy AI? When AI works, it's nothing short of brilliant, helping companies make or save tremendous amounts of money while delighting customers on an unprecedented scale. When it fails, the results can be devastating. Most AI models never make it out of testing, but those failures aren't random. This practical guide to deploying AI lays out a human-first, responsible approach that has seen more than three times the success rate when compared to the industry average. In Real World AI, Alyssa Simpson Rochwerger and Wilson Pang share dozens of AI stories from startups and global enterprises alike featuring personal experiences from people who have worked on global AI deployments that impact billions of people every day.  AI for business doesn't have to be overwhelming. Real World AI uses plain language to walk you through an AI approach that you can feel confident about-for your business and for your customers.

Download or read book Handbook of Deep Learning in Biomedical Engineering written by Valentina Emilia Balas and published by Academic Press. This book was released on 2020-11-12 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning (DL) is a method of machine learning, running over Artificial Neural Networks, that uses multiple layers to extract high-level features from large amounts of raw data. Deep Learning methods apply levels of learning to transform input data into more abstract and composite information. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications gives readers a complete overview of the essential concepts of Deep Learning and its applications in the field of Biomedical Engineering. Deep learning has been rapidly developed in recent years, in terms of both methodological constructs and practical applications. Deep Learning provides computational models of multiple processing layers to learn and represent data with higher levels of abstraction. It is able to implicitly capture intricate structures of large-scale data and is ideally suited to many of the hardware architectures that are currently available. The ever-expanding amount of data that can be gathered through biomedical and clinical information sensing devices necessitates the development of machine learning and AI techniques such as Deep Learning and Convolutional Neural Networks to process and evaluate the data. Some examples of biomedical and clinical sensing devices that use Deep Learning include: Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET), Magnetic Particle Imaging, EE/MEG, Optical Microscopy and Tomography, Photoacoustic Tomography, Electron Tomography, and Atomic Force Microscopy. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications provides the most complete coverage of Deep Learning applications in biomedical engineering available, including detailed real-world applications in areas such as computational neuroscience, neuroimaging, data fusion, medical image processing, neurological disorder diagnosis for diseases such as Alzheimer’s, ADHD, and ASD, tumor prediction, as well as translational multimodal imaging analysis. Presents a comprehensive handbook of the biomedical engineering applications of DL, including computational neuroscience, neuroimaging, time series data such as MRI, functional MRI, CT, EEG, MEG, and data fusion of biomedical imaging data from disparate sources, such as X-Ray/CT Helps readers understand key concepts in DL applications for biomedical engineering and health care, including manifold learning, classification, clustering, and regression in neuroimaging data analysis Provides readers with key DL development techniques such as creation of algorithms and application of DL through artificial neural networks and convolutional neural networks Includes coverage of key application areas of DL such as early diagnosis of specific diseases such as Alzheimer’s, ADHD, and ASD, and tumor prediction through MRI and translational multimodality imaging and biomedical applications such as detection, diagnostic analysis, quantitative measurements, and image guidance of ultrasonography

Download or read book Encyclopedia of Data Science and Machine Learning written by Wang, John and published by IGI Global. This book was released on 2023-01-20 with total page 3296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.