Neural Networks Tricks Of The Trade

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Neural Networks: Tricks of the Trade

Author : Genevieve B. Orr
Publisher : Springer
ISBN 13 : 3540494308
Total Pages : 432 pages
Book Rating : 4.5/5 (44 download)

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Book Synopsis Neural Networks: Tricks of the Trade by : Genevieve B. Orr

Download or read book Neural Networks: Tricks of the Trade written by Genevieve B. Orr and published by Springer. This book was released on 2003-07-31 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is our belief that researchers and practitioners acquire, through experience and word-of-mouth, techniques and heuristics that help them successfully apply neural networks to di cult real world problems. Often these \tricks" are theo- tically well motivated. Sometimes they are the result of trial and error. However, their most common link is that they are usually hidden in people’s heads or in the back pages of space-constrained conference papers. As a result newcomers to the eld waste much time wondering why their networks train so slowly and perform so poorly. This book is an outgrowth of a 1996 NIPS workshop called Tricks of the Trade whose goal was to begin the process of gathering and documenting these tricks. The interest that the workshop generated motivated us to expand our collection and compile it into this book. Although we have no doubt that there are many tricks we have missed, we hope that what we have included will prove to be useful, particularly to those who are relatively new to the eld. Each chapter contains one or more tricks presented by a given author (or authors). We have attempted to group related chapters into sections, though we recognize that the di erent sections are far from disjoint. Some of the chapters (e.g., 1, 13, 17) contain entire systems of tricks that are far more general than the category they have been placed in.

Neural Networks: Tricks of the Trade

Author : Grégoire Montavon
Publisher : Springer
ISBN 13 : 3642352898
Total Pages : 769 pages
Book Rating : 4.6/5 (423 download)

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Book Synopsis Neural Networks: Tricks of the Trade by : Grégoire Montavon

Download or read book Neural Networks: Tricks of the Trade written by Grégoire Montavon and published by Springer. This book was released on 2012-11-14 with total page 769 pages. Available in PDF, EPUB and Kindle. Book excerpt: The twenty last years have been marked by an increase in available data and computing power. In parallel to this trend, the focus of neural network research and the practice of training neural networks has undergone a number of important changes, for example, use of deep learning machines. The second edition of the book augments the first edition with more tricks, which have resulted from 14 years of theory and experimentation by some of the world's most prominent neural network researchers. These tricks can make a substantial difference (in terms of speed, ease of implementation, and accuracy) when it comes to putting algorithms to work on real problems.

Better Deep Learning

Author : Jason Brownlee
Publisher : Machine Learning Mastery
ISBN 13 :
Total Pages : 575 pages
Book Rating : 4./5 ( download)

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Book Synopsis Better Deep Learning by : Jason Brownlee

Download or read book Better Deep Learning written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2018-12-13 with total page 575 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning neural networks have become easy to define and fit, but are still hard to configure. Discover exactly how to improve the performance of deep learning neural network models on your predictive modeling projects. With clear explanations, standard Python libraries, and step-by-step tutorial lessons, you’ll discover how to better train your models, reduce overfitting, and make more accurate predictions.

Advances in Intelligent Signal Processing and Data Mining

Author : Petia Georgieva
Publisher : Springer
ISBN 13 : 3642286968
Total Pages : 354 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Advances in Intelligent Signal Processing and Data Mining by : Petia Georgieva

Download or read book Advances in Intelligent Signal Processing and Data Mining written by Petia Georgieva and published by Springer. This book was released on 2012-07-27 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book presents some of the most efficient statistical and deterministic methods for information processing and applications in order to extract targeted information and find hidden patterns. The techniques presented range from Bayesian approaches and their variations such as sequential Monte Carlo methods, Markov Chain Monte Carlo filters, Rao Blackwellization, to the biologically inspired paradigm of Neural Networks and decomposition techniques such as Empirical Mode Decomposition, Independent Component Analysis and Singular Spectrum Analysis. The book is directed to the research students, professors, researchers and practitioners interested in exploring the advanced techniques in intelligent signal processing and data mining paradigms.

Rough Sets and Knowledge Technology

Author : Duoqian Miao
Publisher : Springer
ISBN 13 : 3319117408
Total Pages : 867 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Rough Sets and Knowledge Technology by : Duoqian Miao

Download or read book Rough Sets and Knowledge Technology written by Duoqian Miao and published by Springer. This book was released on 2014-09-25 with total page 867 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed conference proceedings of the 9th International Conference on Rough Sets and Knowledge Technology, RSKT 2014, held in Shanghai, China, in October 2014. The 70 papers presented were carefully reviewed and selected from 162 submissions. The papers in this volume cover topics such as foundations and generalizations of rough sets, attribute reduction and feature selection, applications of rough sets, intelligent systems and applications, knowledge technology, domain-oriented data-driven data mining, uncertainty in granular computing, advances in granular computing, big data to wise decisions, rough set theory, and three-way decisions, uncertainty, and granular computing.

Computer Networking

Author : Jeanna Matthews
Publisher : John Wiley & Sons
ISBN 13 : 0471661864
Total Pages : 288 pages
Book Rating : 4.4/5 (716 download)

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Book Synopsis Computer Networking by : Jeanna Matthews

Download or read book Computer Networking written by Jeanna Matthews and published by John Wiley & Sons. This book was released on 2005-01-03 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hands-on networking experience, without the lab! The best way to learn about network protocols is to see them in action. But that doesn't mean that you need a lab full of networking equipment. This revolutionary text and its accompanying CD give readers realistic hands-on experience working with network protocols, without requiring all the routers, switches, hubs, and PCs of an actual network. Computer Networking: Internet Protocols in Action provides packet traces of real network activity on CD. Readers open the trace files using Ethereal, an open source network protocol analyzer, and follow the text to perform the exercises, gaining a thorough understanding of the material by seeing it in action. Features * Practicality: Readers are able to learn by doing, without having to use actual networks. Instructors can add an active learning component to their course without the overhead of collecting the materials. * Flexibility: This approach has been used successfully with students at the graduate and undergraduate levels. Appropriate for courses regardless of whether the instructor uses a bottom-up or a top-down approach. * Completeness: The exercises take the reader from the basics of examining quiet and busy networks through application, transport, network, and link layers to the crucial issues of network security.

Neural Network Methods for Natural Language Processing

Author : Yoav Goldberg
Publisher : Springer Nature
ISBN 13 : 3031021657
Total Pages : 20 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Neural Network Methods for Natural Language Processing by : Yoav Goldberg

Download or read book Neural Network Methods for Natural Language Processing written by Yoav Goldberg and published by Springer Nature. This book was released on 2022-06-01 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries. The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.

Advances in Neural Information Processing Systems 8

Author : David S. Touretzky
Publisher : MIT Press
ISBN 13 : 9780262201070
Total Pages : 1128 pages
Book Rating : 4.2/5 (1 download)

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Book Synopsis Advances in Neural Information Processing Systems 8 by : David S. Touretzky

Download or read book Advances in Neural Information Processing Systems 8 written by David S. Touretzky and published by MIT Press. This book was released on 1996 with total page 1128 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decade has seen greatly increased interaction between theoretical work in neuroscience, cognitive science and information processing, and experimental work requiring sophisticated computational modeling. The 152 contributions in NIPS 8 focus on a wide variety of algorithms and architectures for both supervised and unsupervised learning. They are divided into nine parts: Cognitive Science, Neuroscience, Theory, Algorithms and Architectures, Implementations, Speech and Signal Processing, Vision, Applications, and Control. Chapters describe how neuroscientists and cognitive scientists use computational models of neural systems to test hypotheses and generate predictions to guide their work. This work includes models of how networks in the owl brainstem could be trained for complex localization function, how cellular activity may underlie rat navigation, how cholinergic modulation may regulate cortical reorganization, and how damage to parietal cortex may result in neglect. Additional work concerns development of theoretical techniques important for understanding the dynamics of neural systems, including formation of cortical maps, analysis of recurrent networks, and analysis of self- supervised learning. Chapters also describe how engineers and computer scientists have approached problems of pattern recognition or speech recognition using computational architectures inspired by the interaction of populations of neurons within the brain. Examples are new neural network models that have been applied to classical problems, including handwritten character recognition and object recognition, and exciting new work that focuses on building electronic hardware modeled after neural systems. A Bradford Book

Machine Learning for Algorithmic Trading

Author : Stefan Jansen
Publisher : Packt Publishing Ltd
ISBN 13 : 1839216786
Total Pages : 822 pages
Book Rating : 4.8/5 (392 download)

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Book Synopsis Machine Learning for Algorithmic Trading by : Stefan Jansen

Download or read book Machine Learning for Algorithmic Trading written by Stefan Jansen and published by Packt Publishing Ltd. This book was released on 2020-07-31 with total page 822 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.

101 Computing Challenges

Author : Philippe Kerampran
Publisher : Lulu.com
ISBN 13 : 9781291918083
Total Pages : 124 pages
Book Rating : 4.9/5 (18 download)

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Book Synopsis 101 Computing Challenges by : Philippe Kerampran

Download or read book 101 Computing Challenges written by Philippe Kerampran and published by Lulu.com. This book was released on 2014-06-24 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: Boost your programming skills by completing fun and enthusing computing challenges. Learn how to program using HTML, CSS, JavaScript, Scratch, Python and Database software. From ""Hello World"" to complex retro arcade games, choose a challenge based on your abilities and interests. This book is targeted at both learners (from 9 to 99 years old and above) and educators (parents, teachers) who want to adopt a challenging and enthusing approach towards learning about computing concepts whilst developing their programming skills.

Hands-On Neural Network Programming with C#

Author : Matt R. Cole
Publisher : Packt Publishing Ltd
ISBN 13 : 1789619866
Total Pages : 320 pages
Book Rating : 4.7/5 (896 download)

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Book Synopsis Hands-On Neural Network Programming with C# by : Matt R. Cole

Download or read book Hands-On Neural Network Programming with C# written by Matt R. Cole and published by Packt Publishing Ltd. This book was released on 2018-09-29 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Create and unleash the power of neural networks by implementing C# and .Net code Key FeaturesGet a strong foundation of neural networks with access to various machine learning and deep learning librariesReal-world case studies illustrating various neural network techniques and architectures used by practitionersCutting-edge coverage of Deep Networks, optimization algorithms, convolutional networks, autoencoders and many moreBook Description Neural networks have made a surprise comeback in the last few years and have brought tremendous innovation in the world of artificial intelligence. The goal of this book is to provide C# programmers with practical guidance in solving complex computational challenges using neural networks and C# libraries such as CNTK, and TensorFlowSharp. This book will take you on a step-by-step practical journey, covering everything from the mathematical and theoretical aspects of neural networks, to building your own deep neural networks into your applications with the C# and .NET frameworks. This book begins by giving you a quick refresher of neural networks. You will learn how to build a neural network from scratch using packages such as Encog, Aforge, and Accord. You will learn about various concepts and techniques, such as deep networks, perceptrons, optimization algorithms, convolutional networks, and autoencoders. You will learn ways to add intelligent features to your .NET apps, such as facial and motion detection, object detection and labeling, language understanding, knowledge, and intelligent search. Throughout this book, you will be working on interesting demonstrations that will make it easier to implement complex neural networks in your enterprise applications. What you will learnUnderstand perceptrons and how to implement them in C#Learn how to train and visualize a neural network using cognitive servicesPerform image recognition for detecting and labeling objects using C# and TensorFlowSharpDetect specific image characteristics such as a face using Accord.NetDemonstrate particle swarm optimization using a simple XOR problem and EncogTrain convolutional neural networks using ConvNetSharpFind optimal parameters for your neural network functions using numeric and heuristic optimization techniques.Who this book is for This book is for Machine Learning Engineers, Data Scientists, Deep Learning Aspirants and Data Analysts who are now looking to move into advanced machine learning and deep learning with C#. Prior knowledge of machine learning and working experience with C# programming is required to take most out of this book

Connectionist Models

Author : David S. Touretzky
Publisher : Morgan Kaufmann
ISBN 13 : 1483214486
Total Pages : 416 pages
Book Rating : 4.4/5 (832 download)

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Book Synopsis Connectionist Models by : David S. Touretzky

Download or read book Connectionist Models written by David S. Touretzky and published by Morgan Kaufmann. This book was released on 2014-05-12 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Connectionist Models contains the proceedings of the 1990 Connectionist Models Summer School held at the University of California at San Diego. The summer school provided a forum for students and faculty to assess the state of the art with regards to connectionist modeling. Topics covered range from theoretical analysis of networks to empirical investigations of learning algorithms; speech and image processing; cognitive psychology; computational neuroscience; and VLSI design. Comprised of 40 chapters, this book begins with an introduction to mean field, Boltzmann, and Hopfield networks, focusing on deterministic Boltzmann learning in networks with asymmetric connectivity; contrastive Hebbian learning in the continuous Hopfield model; and energy minimization and the satisfiability of propositional logic. Mean field networks that learn to discriminate temporally distorted strings are described. The next sections are devoted to reinforcement learning and genetic learning, along with temporal processing and modularity. Cognitive modeling and symbol processing as well as VLSI implementation are also discussed. This monograph will be of interest to both students and academicians concerned with connectionist modeling.

Integration of Cloud Computing with Internet of Things

Author : Monika Mangla
Publisher : John Wiley & Sons
ISBN 13 : 1119769302
Total Pages : 384 pages
Book Rating : 4.1/5 (197 download)

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Book Synopsis Integration of Cloud Computing with Internet of Things by : Monika Mangla

Download or read book Integration of Cloud Computing with Internet of Things written by Monika Mangla and published by John Wiley & Sons. This book was released on 2021-03-08 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book aims to integrate the aspects of IoT, Cloud computing and data analytics from diversified perspectives. The book also plans to discuss the recent research trends and advanced topics in the field which will be of interest to academicians and researchers working in this area. Thus, the book intends to help its readers to understand and explore the spectrum of applications of IoT, cloud computing and data analytics. Here, it is also worth mentioning that the book is believed to draw attention on the applications of said technology in various disciplines in order to obtain enhanced understanding of the readers. Also, this book focuses on the researches and challenges in the domain of IoT, Cloud computing and Data analytics from perspectives of various stakeholders.

Information Theory, Inference and Learning Algorithms

Author : David J. C. MacKay
Publisher : Cambridge University Press
ISBN 13 : 9780521642989
Total Pages : 694 pages
Book Rating : 4.6/5 (429 download)

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Book Synopsis Information Theory, Inference and Learning Algorithms by : David J. C. MacKay

Download or read book Information Theory, Inference and Learning Algorithms written by David J. C. MacKay and published by Cambridge University Press. This book was released on 2003-09-25 with total page 694 pages. Available in PDF, EPUB and Kindle. Book excerpt: Table of contents

Quantum Dot Sensors

Author : John Callan
Publisher : CRC Press
ISBN 13 : 9814316008
Total Pages : 232 pages
Book Rating : 4.8/5 (143 download)

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Book Synopsis Quantum Dot Sensors by : John Callan

Download or read book Quantum Dot Sensors written by John Callan and published by CRC Press. This book was released on 2013-01-24 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: Consisting of six chapters, written by experts in their field, this book charts the progress made in the use of quantum dots as the signaling component in optical sensors since their discovery in the early 1980s. In particular, it focuses on CdS-, CdSe-, and CdTe-type QDs due to their emission in the visible region of the electromagnetic spectrum. The book begins by detailing the range of methods currently used for the preparation and passivation of core/core–shell quantum dots and follows with a discussion on their electrochemical properties and potential toxicity. The book culminates by focusing on how electron and energy transfer mechanisms can be utilized to generate a range of quantum dot-based probes. This is the first text of its kind dedicated to quantum dot-based sensors and will appeal to those readers who have an interest in working with these versatile nanoparticles.

Fundamentals of Machine Learning

Author : Thomas Trappenberg
Publisher : Oxford University Press
ISBN 13 : 0192563092
Total Pages : 260 pages
Book Rating : 4.1/5 (925 download)

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Book Synopsis Fundamentals of Machine Learning by : Thomas Trappenberg

Download or read book Fundamentals of Machine Learning written by Thomas Trappenberg and published by Oxford University Press. This book was released on 2019-11-28 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: Interest in machine learning is exploding worldwide, both in research and for industrial applications. Machine learning is fast becoming a fundamental part of everyday life. This book is a brief introduction to this area - exploring its importance in a range of many disciplines, from science to engineering, and even its broader impact on our society. The book is written in a style that strikes a balance between brevity of explanation, rigorous mathematical argument, and outlines principle ideas. At the same time, it provides a comprehensive overview of a variety of methods and their application within this field. This includes an introduction to Bayesian approaches to modeling, as well as deep learning. Writing small programs to apply machine learning techniques is made easy by high level programming systems, and this book shows examples in Python with the machine learning libraries 'sklearn' and 'Keras'. The first four chapters concentrate on the practical side of applying machine learning techniques. The following four chapters discuss more fundamental concepts that includes their formulation in a probabilistic context. This is followed by two more chapters on advanced models, that of recurrent neural networks and that of reinforcement learning. The book closes with a brief discussion on the impact of machine learning and AI on our society. Fundamentals of Machine Learning provides a brief and accessible introduction to this rapidly growing field, one that will appeal to students and researchers across computer science and computational neuroscience, as well as the broader cognitive sciences.

Deep Learning for Computer Vision

Author : Jason Brownlee
Publisher : Machine Learning Mastery
ISBN 13 :
Total Pages : 564 pages
Book Rating : 4./5 ( download)

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Book Synopsis Deep Learning for Computer Vision by : Jason Brownlee

Download or read book Deep Learning for Computer Vision written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2019-04-04 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step-by-step tutorials on deep learning neural networks for computer vision in python with Keras.