Share

Advances in Deep Learning

2019-03-14 Technology & Engineering
Download Advances in Deep Learning PDF Online Free

Author :
Release : 2019-03-14
Genre : Technology & Engineering
Kind : eBook
Book Rating : 949/5 ( reviews)

GET EBOOK


Book Synopsis Advances in Deep Learning by : M. Arif Wani

Download or read book Advances in Deep Learning written by M. Arif Wani. This book was released on 2019-03-14. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Advances in Financial Machine Learning

2018-01-23 Business & Economics
Download Advances in Financial Machine Learning PDF Online Free

Author :
Release : 2018-01-23
Genre : Business & Economics
Kind : eBook
Book Rating : 119/5 ( reviews)

GET EBOOK


Book Synopsis Advances in Financial Machine Learning by : Marcos Lopez de Prado

Download or read book Advances in Financial Machine Learning written by Marcos Lopez de Prado. This book was released on 2018-01-23. Available in PDF, EPUB and Kindle. Book excerpt: 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.

New Advances in Machine Learning

2010-02-01 Games & Activities
Download New Advances in Machine Learning PDF Online Free

Author :
Release : 2010-02-01
Genre : Games & Activities
Kind : eBook
Book Rating : 34X/5 ( reviews)

GET EBOOK


Book Synopsis New Advances in Machine Learning by : Yagang Zhang

Download or read book New Advances in Machine Learning written by Yagang Zhang. This book was released on 2010-02-01. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Advances in Machine Learning Applications in Software Engineering

2006-10-31 Computers
Download Advances in Machine Learning Applications in Software Engineering PDF Online Free

Author :
Release : 2006-10-31
Genre : Computers
Kind : eBook
Book Rating : 438/5 ( reviews)

GET EBOOK


Book Synopsis Advances in Machine Learning Applications in Software Engineering by : Zhang, Du

Download or read book Advances in Machine Learning Applications in Software Engineering written by Zhang, Du. This book was released on 2006-10-31. Available in PDF, EPUB and Kindle. Book excerpt: "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.

Probabilistic Machine Learning

2022-03-01 Computers
Download Probabilistic Machine Learning PDF Online Free

Author :
Release : 2022-03-01
Genre : Computers
Kind : eBook
Book Rating : 303/5 ( reviews)

GET EBOOK


Book Synopsis Probabilistic Machine Learning by : Kevin P. Murphy

Download or read book Probabilistic Machine Learning written by Kevin P. Murphy. This book was released on 2022-03-01. Available in PDF, EPUB and Kindle. Book excerpt: 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.

You may also like...