Handbook of Machine Learning Applications for Genomics, Roy
Автор: Goodfellow Ian, Bengio Yoshua, Courville Aaron Название: Deep Learning ISBN: 0262035618 ISBN-13(EAN): 9780262035613 Издательство: MIT Press Рейтинг: Цена: 13543.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -- Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.
The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Автор: David J. Balding, Ida Moltke, John Marioni Название: Handbook of Statistical Genomics ISBN: 1119429145 ISBN-13(EAN): 9781119429142 Издательство: Wiley Рейтинг: Цена: 46086.00 р. Наличие на складе: Поставка под заказ.
Описание: Previous title: Handbook of statistical genetics.
Автор: Pimentel Название: Handbook of Growth Factors (1994) ISBN: 1138105759 ISBN-13(EAN): 9781138105751 Издательство: Taylor&Francis Рейтинг: Цена: 22968.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Volume I of this book provides a comprehensive discussion of the factors involved in regulation of the cell cycle, the general biological properties of growth factors, and the receptor and postreceptor mechanisms of action of these signaling agents.
Автор: Fink, George Название: Stress Genetics, Epigenetics And Genomics ISBN: 012813156X ISBN-13(EAN): 9780128131565 Издательство: Elsevier Science Рейтинг: Цена: 19875.00 р. Наличие на складе: Поставка под заказ.
Описание:
The effect of stress on our emotional and physical health can be devastating. There have been significant advances in our understanding of stress genetics and genomics, and the field of epigenetics has flourished during this time, yet many remain unfamiliar with the latest concepts on these topics. This intense public, research, and clinical interest in stress is reflected in our edited Handbook of Stress series, each volume addressing a specific area within the field of stress edited by experts in each subfield. Stress Genetics, Epigenetics, and Genomics, Volume 4 in the series, covers the influence genetics, epigenetics, and genomics have on physiologic stress and provides a quick orientation to the subject for research, clinic, and everyday life. Integrated closely with new behavioral findings and relevance to human conditions, the concepts and data in this volume offer readers cutting-edge information on the genetics of stress. This volume is of prime interest to neuroscientists, clinicians, researchers, academics, and students in Neuroendocrinology, Neuroscience, Biomedicine, Endocrinology, Psychology, Psychiatry and some aspects of the Social Sciences including stress and its management in the workplace from a preventative, diagnostic, and therapeutic perspective.
Автор: Aboul Ella Hassanien, Ashraf Darwish, Chiranji Lal Chowdhary Название: Handbook of Research on Deep Learning Innovations and Trends ISBN: 1522578625 ISBN-13(EAN): 9781522578628 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 43105.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Leading technology firms and research institutions are continuously exploring new techniques in artificial intelligence and machine learning. As such, deep learning has now been recognized in various real-world applications such as computer vision, image processing, biometrics, pattern recognition, and medical imaging. The deep learning approach has opened new opportunities that can make such real-life applications and tasks easier and more efficient. The Handbook of Research on Deep Learning Innovations and Trends is an essential scholarly resource that presents current trends and the latest research on deep learning and explores the concepts, algorithms, and techniques of data mining and analysis. Highlighting topics such as computer vision, encryption systems, and biometrics, this book is ideal for researchers, practitioners, industry professionals, students, and academicians.
Автор: Mьller Peter Название: Handbook of Dynamics and Probability ISBN: 3030884856 ISBN-13(EAN): 9783030884857 Издательство: Springer Рейтинг: Цена: 23757.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Our time is characterized by an explosive growth in the use of ever more complicated and sophisticated (computer) models. These models rely on dynamical systems theory for the interpretation of their results and on probability theory for the quantification of their uncertainties. A conscientious and intelligent use of these models requires that both these theories are properly understood. This book is to provide such understanding. It gives a unifying treatment of dynamical systems theory and probability theory. It covers the basic concepts and statements of these theories, their interrelations, and their applications to scientific reasoning and physics. The book stresses the underlying concepts and mathematical structures but is written in a simple and illuminating manner without sacrificing too much mathematical rigor. The book is aimed at students, post-docs, and researchers in the applied sciences who aspire to better understand the conceptual and mathematical underpinnings of the models that they use. Despite the peculiarities of any applied science, dynamics and probability are the common and indispensable tools in any modeling effort. The book is self-contained, with many technical aspects covered in appendices, but does require some basic knowledge in analysis, linear algebra, and physics. Peter M?ller, now a professor emeritus at the University of Hawaii, has worked extensively on ocean and climate models and the foundations of complex system theories.
Curious To Know More About Python Programming And Would Like To Go Proficient? Then Learn From The Best Tips and Tricks Laid Down In This Powerful Handbook
If you are already into programming, then you most probably know that there are plenty of platforms to build your code on... but which one is the best and easiest to focus on?
There's No Need To Bang Your Head Against The Wall Anymore...
Python continues to get more attention and becomes one of the world's most popular programming languages. After all, it offers plenty of benefits, such as being versatile and fast to develop.
So far, so good. "And What Am I Supposed To Do When I Barely Know Anything About Python?", you might say...
Straight to the point, eh?
No Worries, You Are Also Covered On That One
This Handy Guide Will Introduce Into Python's Programming Language, Explain All You Need To Know, And Gradually Follow You Through To Build A Great Code At The End Of The Day
With the help of This Book, you will:
- Master The Basic Concepts Of Python Programming and set your way up to code like a pro (don't stress if you have no clue at first, everything you need is included)
- Find A Step-By-Step Guide On How To Use Python and basically do nothing, rather than follow the instructions (so simple)
- Catch On Great Ways To Develop Your Website Creation Skills and get paid to do things while you drink your coffee (that easy)
- Learn How To Build Arbitrary and Optional Arguments and find the best way to handle a circumstance (not many people know these )
- Apply Storing Functions and simultaneously improve the code, and decompose complex problems into simpler pieces
- And There's Much More
Programming might require a different approach and logical thinking according to each situation, but...
Once you learn the basics, everything else will start slowly falling into its place. And With The Help Of This Essential Guide, Python Coding Will Turn Into A Child's Play For You
Curious To Know More About Python Programming And Would Like To Go Proficient? Then Learn From The Best Tips and Tricks Laid Down In This Powerful Handbook!
Название: Handbook of Cluster Analysis ISBN: 1466551887 ISBN-13(EAN): 9781466551886 Издательство: Taylor&Francis Рейтинг: Цена: 33686.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Handbook of Cluster Analysis provides a comprehensive and unified account of the main research developments in cluster analysis. Written by active, distinguished researchers in this area, the book helps readers make informed choices of the most suitable clustering approach for their problem and make better use of existing cluster analysis tools.
The book is organized according to the traditional core approaches to cluster analysis, from the origins to recent developments. After an overview of approaches and a quick journey through the history of cluster analysis, the book focuses on the four major approaches to cluster analysis. These approaches include methods for optimizing an objective function that describes how well data is grouped around centroids, dissimilarity-based methods, mixture models and partitioning models, and clustering methods inspired by nonparametric density estimation. The book also describes additional approaches to cluster analysis, including constrained and semi-supervised clustering, and explores other relevant issues, such as evaluating the quality of a cluster.
This handbook is accessible to readers from various disciplines, reflecting the interdisciplinary nature of cluster analysis. For those already experienced with cluster analysis, the book offers a broad and structured overview. For newcomers to the field, it presents an introduction to key issues. For researchers who are temporarily or marginally involved with cluster analysis problems, the book gives enough algorithmic and practical details to facilitate working knowledge of specific clustering areas.
Автор: Marwala Tshilidzi, Leke Collins Achepsah Название: Handbook Of Machine Learning - Volume 2: Optimization And Decision Making ISBN: 9811205663 ISBN-13(EAN): 9789811205668 Издательство: World Scientific Publishing Рейтинг: Цена: 19008.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Building on Handbook of Machine Learning - Volume 1: Foundation of Artificial Intelligence, this volume on Optimization and Decision Making covers a range of algorithms and their applications. Like the first volume, it provides a starting point for machine learning enthusiasts as a comprehensive guide on classical optimization methods. It also provides an in-depth overview on how artificial intelligence can be used to define, disprove or validate economic modeling and decision making concepts.
Описание: This book discusses the application of machine learning in genomics. Machine Learning offers ample opportunities for Big Data to be assimilated and comprehended effectively using different frameworks. Stratification, diagnosis, classification and survival predictions encompass the different health care regimes representing unique challenges for data pre-processing, model training, refinement of the systems with clinical implications. The book discusses different models for in-depth analysis of different conditions. Machine Learning techniques have revolutionized genomic analysis. Different chapters of the book describe the role of Artificial Intelligence in clinical and genomic diagnostics. It discusses how systems biology is exploited in identifying the genetic markers for drug discovery and disease identification. Myriad number of diseases whether be infectious, metabolic, cancer can be dealt in effectively which combines the different omics data for precision medicine. Major breakthroughs in the field would help reflect more new innovations which are at their pinnacle stage. This book is useful for researchers in the fields of genomics, genetics, computational biology and bioinformatics.
Описание: Data Science, Big Data, and Artificial Intelligence are currently some of the most talked-about concepts in industry, government, and society, and yet also the most misunderstood. This book will clarify these concepts and provide you with practical knowledge to apply them. Featuring:- A comprehensive overview of the various fields of application of data science- Case studies from practice to make the described concepts tangible- Practical examples to help you carry out simple data analysis projectsThe book approaches the topic of data science from several sides. Crucially, it will show you how to build data platforms and apply data science tools and methods. Along the way, it will help you understand - and explain to various stakeholders - how to generate value from these techniques, such as applying data science to help organizations make faster decisions, reduce costs, and open up new markets. Furthermore, it will bring fundamental concepts related to data science to life, including statistics, mathematics, and legal considerations. Finally, the book outlines practical case studies that illustrate how knowledge generated from data is changing various industries over the long term. Contains these current issues:- Mathematics basics: Mathematics for Machine Learning to help you understand and utilize various ML algorithms.- Machine Learning: From statistical to neural and from Transformers and GPT-3 to AutoML, we introduce common frameworks for applying ML in practice- Natural Language Processing: Tools and techniques for gaining insights from text data and developing language technologies- Computer vision: How can we gain insights from images and videos with data science?- Modeling and Simulation: Model the behavior of complex systems, such as the spread of COVID-19, and do a What-If analysis covering different scenarios.- ML and AI in production: How to turn experimentation into a working data science product?- Presenting your results: Essential presentation techniques for data scientistsContributors: Stefan Papp / Wolfgang Weidinger / Katherine Munro / Bernhard Ortner / Annalisa Cadonna / Georg Langs / Roxane Licandro / Mario Meir-Huber / Danko Nikoli? / Zoltan Toth / Barbora Vesela / Rania Wazir / G?nther Zauner
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