Neural Networks Tricks Of The Trade

Author : Genevieve B. Orr,Klaus-Robert Müller
Publisher : Springer
Page : 432 pages
File Size : 41,7 Mb
Release : 2003-07-31
Category : Computers
ISBN : 9783540494300

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Neural Networks: Tricks of the Trade by Genevieve B. Orr,Klaus-Robert Müller Pdf

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.

Author : Grégoire Montavon,Geneviève Orr,Klaus-Robert Müller
Publisher : Springer
Page : 769 pages
File Size : 42,5 Mb
Release : 2012-11-14
Category : Computers
ISBN : 9783642352898

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Neural Networks: Tricks of the Trade by Grégoire Montavon,Geneviève Orr,Klaus-Robert Müller Pdf

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.

Author : Jason Brownlee
Publisher : Machine Learning Mastery
Page : 575 pages
File Size : 43,7 Mb
Release : 2018-12-13
Category : Computers
ISBN : 8210379456XXX

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Better Deep Learning by Jason Brownlee Pdf

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.

Author : Petia Georgieva,Lyudmila Mihaylova,Lakhmi C Jain
Publisher : Springer
Page : 354 pages
File Size : 41,5 Mb
Release : 2012-07-27
Category : Technology & Engineering
ISBN : 9783642286964

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Advances in Intelligent Signal Processing and Data Mining by Petia Georgieva,Lyudmila Mihaylova,Lakhmi C Jain Pdf

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.

Author : David S. Touretzky,Michael C. Mozer,Michael E. Hasselmo
Publisher : MIT Press
Page : 1128 pages
File Size : 44,7 Mb
Release : 1996
Category : Computers
ISBN : 0262201070

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Advances in Neural Information Processing Systems 8 by David S. Touretzky,Michael C. Mozer,Michael E. Hasselmo Pdf

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

Author : Filippo Maria Bianchi,Enrico Maiorino,Michael C. Kampffmeyer,Antonello Rizzi,Robert Jenssen
Publisher : Springer
Page : 72 pages
File Size : 50,5 Mb
Release : 2017-11-09
Category : Computers
ISBN : 9783319703381

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Recurrent Neural Networks for Short-Term Load Forecasting by Filippo Maria Bianchi,Enrico Maiorino,Michael C. Kampffmeyer,Antonello Rizzi,Robert Jenssen Pdf

The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series. Indeed, both service interruptions and resource waste can be reduced with the implementation of an effective forecasting system. Significant research has thus been devoted to the design and development of methodologies for short term load forecasting over the past decades. A class of mathematical models, called Recurrent Neural Networks, are nowadays gaining renewed interest among researchers and they are replacing many practical implementations of the forecasting systems, previously based on static methods. Despite the undeniable expressive power of these architectures, their recurrent nature complicates their understanding and poses challenges in the training procedures. Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet. This work performs a comparative study on the problem of Short-Term Load Forecast, by using different classes of state-of-the-art Recurrent Neural Networks. The authors test the reviewed models first on controlled synthetic tasks and then on different real datasets, covering important practical cases of study. The text also provides a general overview of the most important architectures and defines guidelines for configuring the recurrent networks to predict real-valued time series.

Author : Stefan Jansen
Publisher : Packt Publishing Ltd
Page : 822 pages
File Size : 51,5 Mb
Release : 2020-07-31
Category : Business & Economics
ISBN : 9781839216787

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Machine Learning for Algorithmic Trading by Stefan Jansen Pdf

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.

Author : Monika Mangla,Suneeta Satpathy,Bhagirathi Nayak,Sachi Nandan Mohanty
Publisher : John Wiley & Sons
Page : 384 pages
File Size : 43,5 Mb
Release : 2021-03-08
Category : Computers
ISBN : 9781119769309

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Integration of Cloud Computing with Internet of Things by Monika Mangla,Suneeta Satpathy,Bhagirathi Nayak,Sachi Nandan Mohanty Pdf

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.

Author : Michael R. W. Dawson
Publisher : John Wiley & Sons
Page : 208 pages
File Size : 45,5 Mb
Release : 2008-04-15
Category : Psychology
ISBN : 9781405143899

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Connectionism by Michael R. W. Dawson Pdf

Connectionism is a “hands on” introduction toconnectionist modeling through practical exercises in differenttypes of connectionist architectures. explores three different types of connectionist architectures– distributed associative memory, perceptron, and multilayerperceptron provides a brief overview of each architecture, a detailedintroduction on how to use a program to explore this network, and aseries of practical exercises that are designed to highlight theadvantages, and disadvantages, of each accompanied by a website athttp://www.bcp.psych.ualberta.ca/~mike/Book3/ that includespractice exercises and software, as well as the files and blankexercise sheets required for performing the exercises designed to be used as a stand-alone volume or alongsideMinds and Machines: Connectionism and Psychological Modeling(by Michael R.W. Dawson, Blackwell 2004)

Author : David S. Touretzky,Jeffrey L. Elman,Terrence J. Sejnowski
Publisher : Morgan Kaufmann
Page : 416 pages
File Size : 45,6 Mb
Release : 2014-05-12
Category : Psychology
ISBN : 9781483214481

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Connectionist Models by David S. Touretzky,Jeffrey L. Elman,Terrence J. Sejnowski Pdf

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.

Author : Joanne Quinn,Joanne McEachen,Michael Fullan,Mag Gardner,Max Drummy
Publisher : Corwin Press
Page : 297 pages
File Size : 55,5 Mb
Release : 2019-07-15
Category : Education
ISBN : 9781544385402

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Dive Into Deep Learning by Joanne Quinn,Joanne McEachen,Michael Fullan,Mag Gardner,Max Drummy Pdf

The leading experts in system change and learning, with their school-based partners around the world, have created this essential companion to their runaway best-seller, Deep Learning: Engage the World Change the World. This hands-on guide provides a roadmap for building capacity in teachers, schools, districts, and systems to design deep learning, measure progress, and assess conditions needed to activate and sustain innovation. Dive Into Deep Learning: Tools for Engagement is rich with resources educators need to construct and drive meaningful deep learning experiences in order to develop the kind of mindset and know-how that is crucial to becoming a problem-solving change agent in our global society. Designed in full color, this easy-to-use guide is loaded with tools, tips, protocols, and real-world examples. It includes: • A framework for deep learning that provides a pathway to develop the six global competencies needed to flourish in a complex world — character, citizenship, collaboration, communication, creativity, and critical thinking. • Learning progressions to help educators analyze student work and measure progress. • Learning design rubrics, templates and examples for incorporating the four elements of learning design: learning partnerships, pedagogical practices, learning environments, and leveraging digital. • Conditions rubrics, teacher self-assessment tools, and planning guides to help educators build, mobilize, and sustain deep learning in schools and districts. Learn about, improve, and expand your world of learning. Put the joy back into learning for students and adults alike. Dive into deep learning to create learning experiences that give purpose, unleash student potential, and transform not only learning, but life itself.

Author : Stefanos Kollias
Publisher : Springer Science & Business Media
Page : 1060 pages
File Size : 43,7 Mb
Release : 2006
Category : Artificial intelligence
ISBN : 9783540388715

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Artificial Neural Networks - ICANN 2006 by Stefanos Kollias Pdf

Author : Yoav Goldberg
Publisher : Springer Nature
Page : 20 pages
File Size : 46,8 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031021657

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Neural Network Methods for Natural Language Processing by Yoav Goldberg Pdf

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.

Author : Hamid R. Tizhoosh,M. Ventresca
Publisher : Springer
Page : 328 pages
File Size : 52,9 Mb
Release : 2008-09-08
Category : Technology & Engineering
ISBN : 9783540708292

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Oppositional Concepts in Computational Intelligence by Hamid R. Tizhoosh,M. Ventresca Pdf

“Theoppositeofacorrectstatementisafalsestatement.Buttheopposite of a profound truth may well be another profound truth.” – Niels Bohr This volume is motivated in part by the observation that opposites permeate everything around us, in some form or another. Its study has attracted the attention of countless minds for at least 2500 years. However, due to the lack of an accepted mathematical formalism for opposition it has not been explicitly studiedtoanygreatlengthin?eldsoutsideofphilosophyandlogic.This,despite the fact that we observe opposition everywhere in nature, our minds seem to divide the world into entities and opposite entities; indeed we use opposition everyday. We have become so accustomed to opposition that its existence is accepted, not usually questioned and its importance is constantly overlooked. On one hand, this volume is a ?st attempt to bring together researchers who are inquiring into the complementary nature of systems and processes and, on the other hand, provide some elementary components for a framework to establish a formalism for opposition-based computing. From a computational intelligence perspective, many successful opposition-based concepts have been in existence for a long time. It is not our intention to recast these existing methods, rather to elucidate that, while diverse, they all share the commonality of opposition - in one form or another, either implicitly or explicitly. To this end, we have attempted to provide rough guidelines to understand what makes concepts “oppositional”.

Author : Lars Niklasson,Mikael Boden,Tom Ziemke
Publisher : Springer Science & Business Media
Page : 1197 pages
File Size : 47,5 Mb
Release : 2013-11-11
Category : Computers
ISBN : 9781447115991

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ICANN 98 by Lars Niklasson,Mikael Boden,Tom Ziemke Pdf

ICANN, the International Conference on Artificial Neural Networks, is the official conference series of the European Neural Network Society which started in Helsinki in 1991. Since then ICANN has taken place in Brighton, Amsterdam, Sorrento, Paris, Bochum and Lausanne, and has become Europe's major meeting in the field of neural networks. This book contains the proceedings of ICANN 98, held 2-4 September 1998 in Skovde, Sweden. Of 340 submissions to ICANN 98, 180 were accepted for publication and presentation at the conference. In addition, this book contains seven invited papers presented at the conference. A conference of this size is obviously not organized by three individuals alone. We therefore would like to thank the following people and organizations for supporting ICANN 98 in one way or another: • the European Neural Network Society and the Swedish Neural Network Society for their active support in the organization of this conference, • the Programme Committee and all reviewers for the hard and timely work that was required to produce more than 900 reviews during April 1998, • the Steering Committee which met in Skovde in May 1998 for the final selection of papers and the preparation of the conference program, • the other Module Chairs: Bengt Asker (Industry and Research), Harald Brandt (Applications), Anders Lansner (Computational Neuroscience and Brain Theory), Thorsteinn Rognvaldsson (Theory), Noel Sharkey (co chair Autonomous Robotics and Adaptive Behavior), Bertil Svensson (Hardware and Implementations), • the conference secretary, Leila Khammari, and the rest of the