Deep Neural Networks And Tabular Data

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Deep Neural Networks and Tabular Data

Author : Vadim Borisov
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (139 download)

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Book Synopsis Deep Neural Networks and Tabular Data by : Vadim Borisov

Download or read book Deep Neural Networks and Tabular Data written by Vadim Borisov and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last decade, deep neural networks have enabled remarkable technological advancements, potentially transforming a wide range of aspects of our lives in the future. It is becoming increasingly common for deep-learning models to be used in a variety of situations in the modern life, ranging from search and recommendations to financial and healthcare solutions, and the number of applications utilizing deep neural networks is still on the rise. However, a lot of recent research efforts in deep learning have focused primarily on neural networks and domains in which they excel. This includes computer vision, audio processing, and natural language processing. It is a general tendency for data in these areas to be homogeneous, whereas heterogeneous tabular datasets have received relatively scant attention despite the fact that they are extremely prevalent. In fact, more than half of the datasets on the Google dataset platform are structured and can be represented in a tabular form. The first aim of this study is to provide a thoughtful and comprehensive analysis of deep neural networks' application to modeling and generating tabular data. Apart from that, an open-source performance benchmark on tabular data is presented, where we thoroughly compare over twenty machine and deep learning models on heterogeneous tabular datasets. The second contribution relates to synthetic tabular data generation. Inspired by their success in other homogeneous data modalities, deep generative models such as variational autoencoders and generative adversarial networks are also commonly applied for tabular data generation. However, the use of Transformer-based large language models (which are also generative) for tabular data generation have been received scant research attention. Our contribution to this literature consists of the development of a novel method for generating tabular data based on this family of autoregressive generative models that, on multiple challenging benchmarks, outperformed the current state-of-the-art methods for tabular data generation. Another crucial aspect for a deep-learning data system is that it needs to be reliable and trustworthy to gain broader acceptance in practice, especially in life-critical fields. One of the possible ways to bring trust into a data-driven system is to use explainable machine-learning methods. In spite of this, the current explanation methods often fail to provide robust explanations due to their high sensitivity to the hyperparameter selection or even changes of the random seed. Furthermore, most of these methods are based on feature-wise importance, ignoring the crucial relationship between variables in a sample. The third aim of this work is to address both of these issues by offering more robust and stable explanations, as well as taking into account the relationships between variables using a graph structure. In summary, this thesis made a significant contribution that touched many areas related to deep neural networks and heterogeneous tabular data as well as the usage of explainable machine learning methods.

Deep Learning with Structured Data

Author : Mark Ryan
Publisher : Simon and Schuster
ISBN 13 : 163835717X
Total Pages : 262 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Deep Learning with Structured Data by : Mark Ryan

Download or read book Deep Learning with Structured Data written by Mark Ryan and published by Simon and Schuster. This book was released on 2020-12-08 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases. Summary Deep learning offers the potential to identify complex patterns and relationships hidden in data of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis techniques to the kind of structured, tabular data you'll find in the relational databases that real-world businesses depend on. Filled with practical, relevant applications, this book teaches you how deep learning can augment your existing machine learning and business intelligence systems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Here’s a dirty secret: Half of the time in most data science projects is spent cleaning and preparing data. But there’s a better way: Deep learning techniques optimized for tabular data and relational databases deliver insights and analysis without requiring intense feature engineering. Learn the skills to unlock deep learning performance with much less data filtering, validating, and scrubbing. About the book Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases. Get started using a dataset based on the Toronto transit system. As you work through the book, you’ll learn how easy it is to set up tabular data for deep learning, while solving crucial production concerns like deployment and performance monitoring. What's inside When and where to use deep learning The architecture of a Keras deep learning model Training, deploying, and maintaining models Measuring performance About the reader For readers with intermediate Python and machine learning skills. About the author Mark Ryan is a Data Science Manager at Intact Insurance. He holds a Master's degree in Computer Science from the University of Toronto. Table of Contents 1 Why deep learning with structured data? 2 Introduction to the example problem and Pandas dataframes 3 Preparing the data, part 1: Exploring and cleansing the data 4 Preparing the data, part 2: Transforming the data 5 Preparing and building the model 6 Training the model and running experiments 7 More experiments with the trained model 8 Deploying the model 9 Recommended next steps

Modern Deep Learning for Tabular Data

Author : Andre Ye
Publisher : Apress
ISBN 13 : 9781484286913
Total Pages : 0 pages
Book Rating : 4.2/5 (869 download)

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Book Synopsis Modern Deep Learning for Tabular Data by : Andre Ye

Download or read book Modern Deep Learning for Tabular Data written by Andre Ye and published by Apress. This book was released on 2022-12-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is one of the most powerful tools in the modern artificial intelligence landscape. While having been predominantly applied to highly specialized image, text, and signal datasets, this book synthesizes and presents novel deep learning approaches to a seemingly unlikely domain – tabular data. Whether for finance, business, security, medicine, or countless other domain, deep learning can help mine and model complex patterns in tabular data – an incredibly ubiquitous form of structured data. Part I of the book offers a rigorous overview of machine learning principles, algorithms, and implementation skills relevant to holistically modeling and manipulating tabular data. Part II studies five dominant deep learning model designs – Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Attention and Transformers, and Tree-Rooted Networks – through both their ‘default’ usage and their application to tabular data. Part III compounds the power of the previously covered methods by surveying strategies and techniques to supercharge deep learning systems: autoencoders, deep data generation, meta-optimization, multi-model arrangement, and neural network interpretability. Each chapter comes with extensive visualization, code, and relevant research coverage. Modern Deep Learning for Tabular Data is one of the first of its kind – a wide exploration of deep learning theory and applications to tabular data, integrating and documenting novel methods and techniques in the field. This book provides a strong conceptual and theoretical toolkit to approach challenging tabular data problems. What You Will Learn Important concepts and developments in modern machine learning and deep learning, with a strong emphasis on tabular data applications. Understand the promising links between deep learning and tabular data, and when a deep learning approach is or isn’t appropriate. Apply promising research and unique modeling approaches in real-world data contexts. Explore and engage with modern, research-backed theoretical advances on deep tabular modeling Utilize unique and successful preprocessing methods to prepare tabular data for successful modelling. Who This Book Is ForData scientists and researchers of all levels from beginner to advanced looking to level up results on tabular data with deep learning or to understand the theoretical and practical aspects of deep tabular modeling research. Applicable to readers seeking to apply deep learning to all sorts of complex tabular data contexts, including business, finance, medicine, education, and security.

Neural Networks and Deep Learning

Author : Charu C. Aggarwal
Publisher : Springer
ISBN 13 : 3319944630
Total Pages : 512 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Neural Networks and Deep Learning by : Charu C. Aggarwal

Download or read book Neural Networks and Deep Learning written by Charu C. Aggarwal and published by Springer. This book was released on 2018-08-25 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

Interpretable Machine Learning

Author : Christoph Molnar
Publisher : Lulu.com
ISBN 13 : 0244768528
Total Pages : 320 pages
Book Rating : 4.2/5 (447 download)

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Book Synopsis Interpretable Machine Learning by : Christoph Molnar

Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Deep Learning and the Game of Go

Author : Kevin Ferguson
Publisher : Simon and Schuster
ISBN 13 : 1638354014
Total Pages : 611 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Deep Learning and the Game of Go by : Kevin Ferguson

Download or read book Deep Learning and the Game of Go written by Kevin Ferguson and published by Simon and Schuster. This book was released on 2019-01-06 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game. Foreword by Thore Graepel, DeepMind Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The ancient strategy game of Go is an incredible case study for AI. In 2016, a deep learning-based system shocked the Go world by defeating a world champion. Shortly after that, the upgraded AlphaGo Zero crushed the original bot by using deep reinforcement learning to master the game. Now, you can learn those same deep learning techniques by building your own Go bot! About the Book Deep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. As you progress, you'll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You'll enjoy watching your bot master the game of Go, and along the way, you'll discover how to apply your new deep learning skills to a wide range of other scenarios! What's inside Build and teach a self-improving game AI Enhance classical game AI systems with deep learning Implement neural networks for deep learning About the Reader All you need are basic Python skills and high school-level math. No deep learning experience required. About the Author Max Pumperla and Kevin Ferguson are experienced deep learning specialists skilled in distributed systems and data science. Together, Max and Kevin built the open source bot BetaGo. Table of Contents PART 1 - FOUNDATIONS Toward deep learning: a machine-learning introduction Go as a machine-learning problem Implementing your first Go bot PART 2 - MACHINE LEARNING AND GAME AI Playing games with tree search Getting started with neural networks Designing a neural network for Go data Learning from data: a deep-learning bot Deploying bots in the wild Learning by practice: reinforcement learning Reinforcement learning with policy gradients Reinforcement learning with value methods Reinforcement learning with actor-critic methods PART 3 - GREATER THAN THE SUM OF ITS PARTS AlphaGo: Bringing it all together AlphaGo Zero: Integrating tree search with reinforcement learning

Neural Network for Beginners

Author : Sebastian Klaas
Publisher : BPB Publications
ISBN 13 : 9389423716
Total Pages : 300 pages
Book Rating : 4.3/5 (894 download)

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Book Synopsis Neural Network for Beginners by : Sebastian Klaas

Download or read book Neural Network for Beginners written by Sebastian Klaas and published by BPB Publications. This book was released on 2021-08-24 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: KEY FEATURES ● Understand applications like reinforcement learning, automatic driving and image generation. ● Understand neural networks accompanied with figures and charts. ● Learn about determining coefficients and initial values of weights. DESCRIPTION Deep learning helps you solve issues related to data problems as it has a vast array of mathematical algorithms and has capacity to detect patterns. This book starts with a quick view of deep learning in Python which would include definition, features and applications. You would be learning about perceptron, neural networks, Backpropagation. This book would also give you a clear insight of how to use Numpy and Matplotlin in deep learning models. By the end of the book, you’ll have the knowledge to apply the relevant technologies in deep learning. WHAT YOU WILL LEARN ● To develop deep learning applications, use Python with few outside inputs. ● Study several ideas of profound learning and neural networks ● Learn how to determine coefficients of learning and weight values ● Explore applications such as automation, image generation and reinforcement learning ● Implement trends like batch Normalisation, dropout, and Adam WHO THIS BOOK IS FOR Deep Learning from the Basics is for data scientists, data analysts and developers who wish to build efficient solutions by applying deep learning techniques. Individuals who would want a better grasp of technology and an overview. You should have a workable Python knowledge is a required. NumPy knowledge and pandas will be an advantage, but that’s completely optional. TABLE OF CONTENTS 1. Python Introduction 2. Perceptron in Depth 3. Neural Networks 4. Training Neural Network 5. Backpropagation 6. Neural Network Training Techniques 7. CNN 8. Deep Learning

Deep Learning: Convergence to Big Data Analytics

Author : Murad Khan
Publisher : Springer
ISBN 13 : 9811334595
Total Pages : 79 pages
Book Rating : 4.8/5 (113 download)

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Book Synopsis Deep Learning: Convergence to Big Data Analytics by : Murad Khan

Download or read book Deep Learning: Convergence to Big Data Analytics written by Murad Khan and published by Springer. This book was released on 2018-12-30 with total page 79 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used to analyze big data in real-time, such as Hadoop, SPARK, and GRAPHX. Big data analytics using traditional techniques face various challenges, such as fast, accurate and efficient processing of big data in real-time. In addition, the Internet of Things is progressively increasing in various fields, like smart cities, smart homes, and e-health. As the enormous number of connected devices generate huge amounts of data every day, we need sophisticated algorithms to deal, organize, and classify this data in less processing time and space. Similarly, existing techniques and algorithms for deep learning in big data field have several advantages thanks to the two main branches of the deep learning, i.e. convolution and deep belief networks. This book offers insights into these techniques and applications based on these two types of deep learning. Further, it helps students, researchers, and newcomers understand big data analytics based on deep learning approaches. It also discusses various machine learning techniques in concatenation with the deep learning paradigm to support high-end data processing, data classifications, and real-time data processing issues. The classification and presentation are kept quite simple to help the readers and students grasp the basics concepts of various deep learning paradigms and frameworks. It mainly focuses on theory rather than the mathematical background of the deep learning concepts. The book consists of 5 chapters, beginning with an introductory explanation of big data and deep learning techniques, followed by integration of big data and deep learning techniques and lastly the future directions.

Multi-faceted Deep Learning

Author : Jenny Benois-Pineau
Publisher : Springer Nature
ISBN 13 : 3030744787
Total Pages : 321 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Multi-faceted Deep Learning by : Jenny Benois-Pineau

Download or read book Multi-faceted Deep Learning written by Jenny Benois-Pineau and published by Springer Nature. This book was released on 2021-10-20 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers a large set of methods in the field of Artificial Intelligence - Deep Learning applied to real-world problems. The fundamentals of the Deep Learning approach and different types of Deep Neural Networks (DNNs) are first summarized in this book, which offers a comprehensive preamble for further problem–oriented chapters. The most interesting and open problems of machine learning in the framework of Deep Learning are discussed in this book and solutions are proposed. This book illustrates how to implement the zero-shot learning with Deep Neural Network Classifiers, which require a large amount of training data. The lack of annotated training data naturally pushes the researchers to implement low supervision algorithms. Metric learning is a long-term research but in the framework of Deep Learning approaches, it gets freshness and originality. Fine-grained classification with a low inter-class variability is a difficult problem for any classification tasks. This book presents how it is solved, by using different modalities and attention mechanisms in 3D convolutional networks. Researchers focused on Machine Learning, Deep learning, Multimedia and Computer Vision will want to buy this book. Advanced level students studying computer science within these topic areas will also find this book useful.

Deep Learning for Coders with fastai and PyTorch

Author : Jeremy Howard
Publisher : O'Reilly Media
ISBN 13 : 1492045497
Total Pages : 624 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Deep Learning for Coders with fastai and PyTorch by : Jeremy Howard

Download or read book Deep Learning for Coders with fastai and PyTorch written by Jeremy Howard and published by O'Reilly Media. This book was released on 2020-06-29 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications

Author : Management Association, Information Resources
Publisher : IGI Global
ISBN 13 : 1799804151
Total Pages : 1671 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications by : Management Association, Information Resources

Download or read book Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications written by Management Association, Information Resources and published by IGI Global. This book was released on 2019-10-11 with total page 1671 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. Deep learning, a subset of artificial intelligence and machine learning, has been recognized in various real-world applications such as computer vision, image processing, and pattern recognition. The deep learning approach has opened new opportunities that can make such real-life applications and tasks easier and more efficient. Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications is a vital reference source that trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. It also explores the latest concepts, algorithms, and techniques of deep learning and data mining and analysis. Highlighting a range of topics such as natural language processing, predictive analytics, and deep neural networks, this multi-volume book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the latest trends in the field of deep learning.

Deep Learning

Author : Ian Goodfellow
Publisher : MIT Press
ISBN 13 : 0262337371
Total Pages : 801 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Deep Learning by : Ian Goodfellow

Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-10 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Neural Networks and Deep Learning

Author : Charu C. Aggarwal
Publisher : Springer Nature
ISBN 13 : 3031296427
Total Pages : 542 pages
Book Rating : 4.0/5 (312 download)

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Book Synopsis Neural Networks and Deep Learning by : Charu C. Aggarwal

Download or read book Neural Networks and Deep Learning written by Charu C. Aggarwal and published by Springer Nature. This book was released on 2023-06-29 with total page 542 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Deep learning methods for various data domains, such as text, images, and graphs are presented in detail. The chapters of this book span three categories: The basics of neural networks: The backpropagation algorithm is discussed in Chapter 2. Many traditional machine learning models can be understood as special cases of neural networks. Chapter 3 explores the connections between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 4 and 5. Chapters 6 and 7 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 8, 9, and 10 discuss recurrent neural networks, convolutional neural networks, and graph neural networks. Several advanced topics like deep reinforcement learning, attention mechanisms, transformer networks, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 11 and 12. The textbook is written for graduate students and upper under graduate level students. Researchers and practitioners working within this related field will want to purchase this as well. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques. The second edition is substantially reorganized and expanded with separate chapters on backpropagation and graph neural networks. Many chapters have been significantly revised over the first edition. Greater focus is placed on modern deep learning ideas such as attention mechanisms, transformers, and pre-trained language models.

Math for Deep Learning

Author : Ronald T. Kneusel
Publisher : No Starch Press
ISBN 13 : 1718501919
Total Pages : 346 pages
Book Rating : 4.7/5 (185 download)

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Book Synopsis Math for Deep Learning by : Ronald T. Kneusel

Download or read book Math for Deep Learning written by Ronald T. Kneusel and published by No Starch Press. This book was released on 2021-11-23 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: Math for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and better use the deep learning toolkits. With Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning. You’ll work through Python examples to learn key deep learning related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in a neural network, backpropagation, and gradient descent. You’ll also use Python to work through the mathematics that underlies those algorithms and even build a fully-functional neural network. In addition you’ll find coverage of gradient descent including variations commonly used by the deep learning community: SGD, Adam, RMSprop, and Adagrad/Adadelta.

Codeless Deep Learning with KNIME

Author : Kathrin Melcher
Publisher : Packt Publishing Ltd
ISBN 13 : 180056242X
Total Pages : 385 pages
Book Rating : 4.8/5 (5 download)

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Book Synopsis Codeless Deep Learning with KNIME by : Kathrin Melcher

Download or read book Codeless Deep Learning with KNIME written by Kathrin Melcher and published by Packt Publishing Ltd. This book was released on 2020-11-27 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover how to integrate KNIME Analytics Platform with deep learning libraries to implement artificial intelligence solutions Key FeaturesBecome well-versed with KNIME Analytics Platform to perform codeless deep learningDesign and build deep learning workflows quickly and more easily using the KNIME GUIDiscover different deployment options without using a single line of code with KNIME Analytics PlatformBook Description KNIME Analytics Platform is an open source software used to create and design data science workflows. This book is a comprehensive guide to the KNIME GUI and KNIME deep learning integration, helping you build neural network models without writing any code. It’ll guide you in building simple and complex neural networks through practical and creative solutions for solving real-world data problems. Starting with an introduction to KNIME Analytics Platform, you’ll get an overview of simple feed-forward networks for solving simple classification problems on relatively small datasets. You’ll then move on to build, train, test, and deploy more complex networks, such as autoencoders, recurrent neural networks (RNNs), long short-term memory (LSTM), and convolutional neural networks (CNNs). In each chapter, depending on the network and use case, you’ll learn how to prepare data, encode incoming data, and apply best practices. By the end of this book, you’ll have learned how to design a variety of different neural architectures and will be able to train, test, and deploy the final network. What you will learnUse various common nodes to transform your data into the right structure suitable for training a neural networkUnderstand neural network techniques such as loss functions, backpropagation, and hyperparametersPrepare and encode data appropriately to feed it into the networkBuild and train a classic feedforward networkDevelop and optimize an autoencoder network for outlier detectionImplement deep learning networks such as CNNs, RNNs, and LSTM with the help of practical examplesDeploy a trained deep learning network on real-world dataWho this book is for This book is for data analysts, data scientists, and deep learning developers who are not well-versed in Python but want to learn how to use KNIME GUI to build, train, test, and deploy neural networks with different architectures. The practical implementations shown in the book do not require coding or any knowledge of dedicated scripts, so you can easily implement your knowledge into practical applications. No prior experience of using KNIME is required to get started with this book.

Deep Learning

Author : Siddhartha Bhattacharyya
Publisher : Walter de Gruyter GmbH & Co KG
ISBN 13 : 3110670909
Total Pages : 161 pages
Book Rating : 4.1/5 (16 download)

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Book Synopsis Deep Learning by : Siddhartha Bhattacharyya

Download or read book Deep Learning written by Siddhartha Bhattacharyya and published by Walter de Gruyter GmbH & Co KG. This book was released on 2020-06-22 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep learning is a new area of machine learning research which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non-immediately related fields, for example between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g. classification) and/or unsupervised (e.g. pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition.

Deep Learning for Data Analytics

Author : Himansu Das
Publisher : Academic Press
ISBN 13 : 0128226080
Total Pages : 220 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Deep Learning for Data Analytics by : Himansu Das

Download or read book Deep Learning for Data Analytics written by Himansu Das and published by Academic Press. This book was released on 2020-05-29 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear processing, which aids in feature extraction and learning in supervised and unsupervised ways, including classification and pattern analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning algorithms extract high level complex data and process these complex sets to relatively simpler ideas formulated in the preceding level of the hierarchy. The authors of this book focus on suitable data analytics methods to solve complex real world problems such as medical image recognition, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinoformatics, audio recognition, drug design, and medical image analysis. Presents the latest advances in Deep Learning for data analytics and biomedical engineering applications. Discusses Deep Learning techniques as they are being applied in the real world of biomedical engineering and data science, including Deep Learning networks, deep feature learning, deep learning toolboxes, performance evaluation, Deep Learning optimization, deep auto-encoders, and deep neural networks Provides readers with an introduction to Deep Learning, along with coverage of deep belief networks, convolutional neural networks, Restricted Boltzmann Machines, data analytics basics, enterprise data science, predictive analysis, optimization for Deep Learning, and feature selection using Deep Learning