Advances In Machine Learning Ii

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Advances in Financial Machine Learning

Marcos Lopez de Prado
Advances in Financial Machine Learning Book PDF

Author : Marcos Lopez de Prado
Publisher : John Wiley & Sons
Page : 400 pages
File Size : 44,8 Mb
Release : 2018-01-23
Category : Business & Economics
ISBN : 9781119482116

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Advances in Financial Machine Learning by Marcos Lopez de Prado Pdf

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

Probabilistic Machine Learning

Kevin P. Murphy
Probabilistic Machine Learning Book PDF

Author : Kevin P. Murphy
Publisher : MIT Press
Page : 858 pages
File Size : 46,9 Mb
Release : 2022-03-01
Category : Computers
ISBN : 9780262369305

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Probabilistic Machine Learning by Kevin P. Murphy Pdf

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.

Advances in Deep Learning

M. Arif Wani,Farooq Ahmad Bhat,Saduf Afzal,Asif Iqbal Khan
Advances in Deep Learning Book PDF

Author : M. Arif Wani,Farooq Ahmad Bhat,Saduf Afzal,Asif Iqbal Khan
Publisher : Springer
Page : 149 pages
File Size : 48,7 Mb
Release : 2019-03-14
Category : Technology & Engineering
ISBN : 9789811367946

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Advances in Deep Learning by M. Arif Wani,Farooq Ahmad Bhat,Saduf Afzal,Asif Iqbal Khan Pdf

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.

Machine Learning

Roger Inge,Jan Leif
Machine Learning Book PDF

Author : Roger Inge,Jan Leif
Publisher : Nova Publishers
Page : 0 pages
File Size : 53,7 Mb
Release : 2017
Category : Machine learning
ISBN : 1536125709

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Machine Learning by Roger Inge,Jan Leif Pdf

In chapter one, Lei Jia, PhD and Hua Gao, PhD analyze machine learning applications in small molecule and macromolecule drug discovery and development while comparing the similarities and differences between the two. They also examine their advantages and limitations with the intent to encourage further creative machine learning applications in drug discovery and development. During chapter two, Oscar Claveria, Enric Monte, and Salvador Torra present a study on the extrapolative performance of several machine learning models in a multiple-input multiple-output setting that permits cross-correlation between the inputs. Bojan Ploj, Germano Resconi, and Ali Yaghoubi parallel the solution of a system by logic gates and by a neural network, in which considerations are computed by the designated one step method during chapter three. In chapter four, Loris Nannia, Nicolò Zaffonatoa, Christian Salvatoreb, Isabella Castiglionib, and the Alzheimers Disease Neuroimaging Initiative propose a method that could aid in the early diagnosis of Alzheimers disease. Afterwards, F. Dornaika and I. Kamal Aldine present and experimentally assess two non-linear data self-representativeness coding schemes based on Hilbert space and column generation. Lastly, Christos Chrysoulas, Grigorios Kalliatakis, and Georgios Stamatiadis give an overview of Apache Hadoop, an open-source software framework used to distribute storage and process big data using the MapReduce programming model.

Advances in Machine Learning II

Jacek Koronacki,Zbigniew W. Ras,Slawomir T. Wierzchon
Advances in Machine Learning II Book PDF

Author : Jacek Koronacki,Zbigniew W. Ras,Slawomir T. Wierzchon
Publisher : Springer Science & Business Media
Page : 530 pages
File Size : 41,5 Mb
Release : 2009-12-24
Category : Computers
ISBN : 9783642051784

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Advances in Machine Learning II by Jacek Koronacki,Zbigniew W. Ras,Slawomir T. Wierzchon Pdf

This is the second volume of a large two-volume editorial project we wish to dedicate to the memory of the late Professor Ryszard S. Michalski who passed away in 2007. He was one of the fathers of machine learning, an exciting and relevant, both from the practical and theoretical points of view, area in modern computer science and information technology. His research career started in the mid-1960s in Poland, in the Institute of Automation, Polish Academy of Sciences in Warsaw, Poland. He left for the USA in 1970, and since then had worked there at various universities, notably, at the University of Illinois at Urbana – Champaign and finally, until his untimely death, at George Mason University. We, the editors, had been lucky to be able to meet and collaborate with Ryszard for years, indeed some of us knew him when he was still in Poland. After he started working in the USA, he was a frequent visitor to Poland, taking part at many conferences until his death. We had also witnessed with a great personal pleasure honors and awards he had received over the years, notably when some years ago he was elected Foreign Member of the Polish Academy of Sciences among some top scientists and scholars from all over the world, including Nobel prize winners. Professor Michalski’s research results influenced very strongly the development of machine learning, data mining, and related areas. Also, he inspired many established and younger scholars and scientists all over the world. We feel very happy that so many top scientists from all over the world agreed to pay the last tribute to Professor Michalski by writing papers in their areas of research. These papers will constitute the most appropriate tribute to Professor Michalski, a devoted scholar and researcher. Moreover, we believe that they will inspire many newcomers and younger researchers in the area of broadly perceived machine learning, data analysis and data mining. The papers included in the two volumes, Machine Learning I and Machine Learning II, cover diverse topics, and various aspects of the fields involved. For convenience of the potential readers, we will now briefly summarize the contents of the particular chapters.

Machine Learning Paradigms

Maria Virvou,Efthimios Alepis,George A. Tsihrintzis,Lakhmi C. Jain
Machine Learning Paradigms Book PDF

Author : Maria Virvou,Efthimios Alepis,George A. Tsihrintzis,Lakhmi C. Jain
Publisher : Springer
Page : 223 pages
File Size : 49,7 Mb
Release : 2019-03-16
Category : Technology & Engineering
ISBN : 9783030137434

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Machine Learning Paradigms by Maria Virvou,Efthimios Alepis,George A. Tsihrintzis,Lakhmi C. Jain Pdf

This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators’ and learners’ data with the goal of improving education and learning systems. In this context, internationally respected researchers present various aspects of learning analytics and selected application areas, including: • Using learning analytics to measure student engagement, to quantify the learning experience and to facilitate self-regulation; • Using learning analytics to predict student performance; • Using learning analytics to create learning materials and educational courses; and • Using learning analytics as a tool to support learners and educators in synchronous and asynchronous eLearning. The book offers a valuable asset for professors, researchers, scientists, engineers and students of all disciplines. Extensive bibliographies at the end of each chapter guide readers to probe further into their application areas of interest.

New Advances in Machine Learning

Yagang Zhang
New Advances in Machine Learning Book PDF

Author : Yagang Zhang
Publisher : BoD – Books on Demand
Page : 375 pages
File Size : 52,5 Mb
Release : 2010-02-01
Category : Games & Activities
ISBN : 9789533070346

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New Advances in Machine Learning by Yagang Zhang Pdf

The purpose of this book is to provide an up-to-date and systematical introduction to the principles and algorithms of machine learning. The definition of learning is broad enough to include most tasks that we commonly call “learning” tasks, as we use the word in daily life. It is also broad enough to encompass computers that improve from experience in quite straightforward ways. The book will be of interest to industrial engineers and scientists as well as academics who wish to pursue machine learning. The book is intended for both graduate and postgraduate students in fields such as computer science, cybernetics, system sciences, engineering, statistics, and social sciences, and as a reference for software professionals and practitioners. The wide scope of the book provides a good introduction to many approaches of machine learning, and it is also the source of useful bibliographical information.

Artificial Intelligence By Example

Denis Rothman
Artificial Intelligence By Example Book PDF

Author : Denis Rothman
Publisher : Packt Publishing Ltd
Page : 579 pages
File Size : 42,9 Mb
Release : 2020-02-28
Category : Computers
ISBN : 9781839212819

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Artificial Intelligence By Example by Denis Rothman Pdf

Understand the fundamentals and develop your own AI solutions in this updated edition packed with many new examples Key FeaturesAI-based examples to guide you in designing and implementing machine intelligenceBuild machine intelligence from scratch using artificial intelligence examplesDevelop machine intelligence from scratch using real artificial intelligenceBook Description AI has the potential to replicate humans in every field. Artificial Intelligence By Example, Second Edition serves as a starting point for you to understand how AI is built, with the help of intriguing and exciting examples. This book will make you an adaptive thinker and help you apply concepts to real-world scenarios. Using some of the most interesting AI examples, right from computer programs such as a simple chess engine to cognitive chatbots, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and Internet of Things (IoT), and develop emotional quotient in chatbots using neural networks such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs). This edition also has new examples for hybrid neural networks, combining reinforcement learning (RL) and deep learning (DL), chained algorithms, combining unsupervised learning with decision trees, random forests, combining DL and genetic algorithms, conversational user interfaces (CUI) for chatbots, neuromorphic computing, and quantum computing. By the end of this book, you will understand the fundamentals of AI and have worked through a number of examples that will help you develop your AI solutions. What you will learnApply k-nearest neighbors (KNN) to language translations and explore the opportunities in Google TranslateUnderstand chained algorithms combining unsupervised learning with decision treesSolve the XOR problem with feedforward neural networks (FNN) and build its architecture to represent a data flow graphLearn about meta learning models with hybrid neural networksCreate a chatbot and optimize its emotional intelligence deficiencies with tools such as Small Talk and data loggingBuilding conversational user interfaces (CUI) for chatbotsWriting genetic algorithms that optimize deep learning neural networksBuild quantum computing circuitsWho this book is for Developers and those interested in AI, who want to understand the fundamentals of Artificial Intelligence and implement them practically. Prior experience with Python programming and statistical knowledge is essential to make the most out of this book.

Advances in Machine Learning Applications in Software Engineering

Zhang, Du,Tsai, Jeffery J.P.
Advances in Machine Learning Applications in Software Engineering Book PDF

Author : Zhang, Du,Tsai, Jeffery J.P.
Publisher : IGI Global
Page : 498 pages
File Size : 44,8 Mb
Release : 2006-10-31
Category : Computers
ISBN : 9781591409434

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Advances in Machine Learning Applications in Software Engineering by Zhang, Du,Tsai, Jeffery J.P. Pdf

"This book provides analysis, characterization and refinement of software engineering data in terms of machine learning methods. It depicts applications of several machine learning approaches in software systems development and deployment, and the use of machine learning methods to establish predictive models for software quality while offering readers suggestions by proposing future work in this emerging research field"--Provided by publisher.

Advances in Machine Learning/Deep Learning-based Technologies

George A. Tsihrintzis,Maria Virvou,Lakhmi C. Jain
Advances in Machine Learning Deep Learning based Technologies Book PDF

Author : George A. Tsihrintzis,Maria Virvou,Lakhmi C. Jain
Publisher : Springer Nature
Page : 237 pages
File Size : 43,9 Mb
Release : 2021-08-05
Category : Technology & Engineering
ISBN : 9783030767945

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Advances in Machine Learning/Deep Learning-based Technologies by George A. Tsihrintzis,Maria Virvou,Lakhmi C. Jain Pdf

As the 4th Industrial Revolution is restructuring human societal organization into, so-called, “Society 5.0”, the field of Machine Learning (and its sub-field of Deep Learning) and related technologies is growing continuously and rapidly, developing in both itself and towards applications in many other disciplines. Researchers worldwide aim at incorporating cognitive abilities into machines, such as learning and problem solving. When machines and software systems have been enhanced with Machine Learning/Deep Learning components, they become better and more efficient at performing specific tasks. Consequently, Machine Learning/Deep Learning stands out as a research discipline due to its worldwide pace of growth in both theoretical advances and areas of application, while achieving very high rates of success and promising major impact in science, technology and society. The book at hand aims at exposing its readers to some of the most significant Advances in Machine Learning/Deep Learning-based Technologies. The book consists of an editorial note and an additional ten (10) chapters, all invited from authors who work on the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into five parts, namely (i) Machine Learning/Deep Learning in Socializing and Entertainment, (ii) Machine Learning/Deep Learning in Education, (iii) Machine Learning/Deep Learning in Security, (iv) Machine Learning/Deep Learning in Time Series Forecasting, and (v) Machine Learning in Video Coding and Information Extraction. This research book is directed towards professors, researchers, scientists, engineers and students in Machine Learning/Deep Learning-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent Machine Learning/Deep Learning-based technologies. An extensive list of bibliographic references at the end of each chapter guides the readers to probe further into the application areas of interest to them.

Machine Learning in Finance

Matthew F. Dixon,Igor Halperin,Paul Bilokon
Machine Learning in Finance Book PDF

Author : Matthew F. Dixon,Igor Halperin,Paul Bilokon
Publisher : Springer Nature
Page : 565 pages
File Size : 54,9 Mb
Release : 2020-07-01
Category : Business & Economics
ISBN : 9783030410681

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Machine Learning in Finance by Matthew F. Dixon,Igor Halperin,Paul Bilokon Pdf

This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.

Advanced Machine Learning with Python

John Hearty
Advanced Machine Learning with Python Book PDF

Author : John Hearty
Publisher : Packt Publishing Ltd
Page : 278 pages
File Size : 54,6 Mb
Release : 2016-07-28
Category : Computers
ISBN : 9781784393830

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Advanced Machine Learning with Python by John Hearty Pdf

Solve challenging data science problems by mastering cutting-edge machine learning techniques in Python About This Book Resolve complex machine learning problems and explore deep learning Learn to use Python code for implementing a range of machine learning algorithms and techniques A practical tutorial that tackles real-world computing problems through a rigorous and effective approach Who This Book Is For This title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution, or of entering a Kaggle contest for instance, this book is for you! Prior experience of Python and grounding in some of the core concepts of machine learning would be helpful. What You Will Learn Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms Apply your new found skills to solve real problems, through clearly-explained code for every technique and test Automate large sets of complex data and overcome time-consuming practical challenges Improve the accuracy of models and your existing input data using powerful feature engineering techniques Use multiple learning techniques together to improve the consistency of results Understand the hidden structure of datasets using a range of unsupervised techniques Gain insight into how the experts solve challenging data problems with an effective, iterative, and validation-focused approach Improve the effectiveness of your deep learning models further by using powerful ensembling techniques to strap multiple models together In Detail Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data. The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce. This book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semi-supervised learning and more, in real world applications. We will also learn about NumPy and Theano. By this end of this book, you will learn a set of advanced Machine Learning techniques and acquire a broad set of powerful skills in the area of feature selection & feature engineering. Style and approach This book focuses on clarifying the theory and code behind complex algorithms to make them practical, useable, and well-understood. Each topic is described with real-world applications, providing both broad contextual coverage and detailed guidance.

Advances in Machine Learning/Deep Learning-based Technologies

George A. Tsihrintzis,Maria Virvou,Lakhmi C. Jain
Advances in Machine Learning Deep Learning based Technologies Book PDF

Author : George A. Tsihrintzis,Maria Virvou,Lakhmi C. Jain
Publisher : Springer
Page : 226 pages
File Size : 40,8 Mb
Release : 2021-10-16
Category : Technology & Engineering
ISBN : 3030767930

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Advances in Machine Learning/Deep Learning-based Technologies by George A. Tsihrintzis,Maria Virvou,Lakhmi C. Jain Pdf

As the 4th Industrial Revolution is restructuring human societal organization into, so-called, “Society 5.0”, the field of Machine Learning (and its sub-field of Deep Learning) and related technologies is growing continuously and rapidly, developing in both itself and towards applications in many other disciplines. Researchers worldwide aim at incorporating cognitive abilities into machines, such as learning and problem solving. When machines and software systems have been enhanced with Machine Learning/Deep Learning components, they become better and more efficient at performing specific tasks. Consequently, Machine Learning/Deep Learning stands out as a research discipline due to its worldwide pace of growth in both theoretical advances and areas of application, while achieving very high rates of success and promising major impact in science, technology and society. The book at hand aims at exposing its readers to some of the most significant Advances in Machine Learning/Deep Learning-based Technologies. The book consists of an editorial note and an additional ten (10) chapters, all invited from authors who work on the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into five parts, namely (i) Machine Learning/Deep Learning in Socializing and Entertainment, (ii) Machine Learning/Deep Learning in Education, (iii) Machine Learning/Deep Learning in Security, (iv) Machine Learning/Deep Learning in Time Series Forecasting, and (v) Machine Learning in Video Coding and Information Extraction. This research book is directed towards professors, researchers, scientists, engineers and students in Machine Learning/Deep Learning-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent Machine Learning/Deep Learning-based technologies. An extensive list of bibliographic references at the end of each chapter guides the readers to probe further into the application areas of interest to them.

Advances in Machine Learning and Data Mining for Astronomy

Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava
Advances in Machine Learning and Data Mining for Astronomy Book PDF

Author : Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava
Publisher : CRC Press
Page : 744 pages
File Size : 40,5 Mb
Release : 2012-03-29
Category : Computers
ISBN : 9781439841747

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Advances in Machine Learning and Data Mining for Astronomy by Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava Pdf

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines

Advances in Deep Learning, Artificial Intelligence and Robotics

Luigi Troiano,Alfredo Vaccaro,Roberto Tagliaferri,Nishtha Kesswani,Irene Díaz Rodriguez,Imene Brigui,Domenico Parente
Advances in Deep Learning Artificial Intelligence and Robotics Book PDF

Author : Luigi Troiano,Alfredo Vaccaro,Roberto Tagliaferri,Nishtha Kesswani,Irene Díaz Rodriguez,Imene Brigui,Domenico Parente
Publisher : Springer Nature
Page : 235 pages
File Size : 49,8 Mb
Release : 2022-01-03
Category : Technology & Engineering
ISBN : 9783030853655

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Advances in Deep Learning, Artificial Intelligence and Robotics by Luigi Troiano,Alfredo Vaccaro,Roberto Tagliaferri,Nishtha Kesswani,Irene Díaz Rodriguez,Imene Brigui,Domenico Parente Pdf

This book of Advances in Deep Learning, Artificial Intelligence and Robotics (proceedings of ICDLAIR 2020) is intended to be used as a reference by students and researchers who collect scientific and technical contributions with respect to models, tools, technologies and applications in the field of modern artificial intelligence and robotics. Deep Learning, AI and robotics represent key ingredients for the 4th Industrial Revolution. Their extensive application is dramatically changing products and services, with a large impact on labour, economy and society at all. The research and reports of new technologies and applications in DL, AI and robotics like biometric recognition systems, medical diagnosis, industries, telecommunications, AI petri nets model-based diagnosis, gaming, stock trading, intelligent aerospace systems, robot control and web intelligence aim to bridge the gap between these non-coherent disciplines of knowledge and fosters unified development in next-generation computational models for machine intelligence.