Modern Deep Learning For Tabular Data
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Modern Deep Learning For Tabular Data
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Author : Andre Ye
language : en
Publisher: Apress
Release Date : 2022-12-27
Modern Deep Learning For Tabular Data written by Andre Ye and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-27 with Computers categories.
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.
Deep Neural Networks And Tabular Data
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Author : Vadim Borisov
language : en
Publisher:
Release Date : 2023
Deep Neural Networks And Tabular Data written by Vadim Borisov and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
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.
Modern Time Series Forecasting With Python
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Author : Manu Joseph
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-11-24
Modern Time Series Forecasting With Python written by Manu Joseph and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-24 with Computers categories.
Build real-world time series forecasting systems which scale to millions of time series by applying modern machine learning and deep learning concepts Key Features Explore industry-tested machine learning techniques used to forecast millions of time series Get started with the revolutionary paradigm of global forecasting models Get to grips with new concepts by applying them to real-world datasets of energy forecasting Book DescriptionWe live in a serendipitous era where the explosion in the quantum of data collected and a renewed interest in data-driven techniques such as machine learning (ML), has changed the landscape of analytics, and with it, time series forecasting. This book, filled with industry-tested tips and tricks, takes you beyond commonly used classical statistical methods such as ARIMA and introduces to you the latest techniques from the world of ML. This is a comprehensive guide to analyzing, visualizing, and creating state-of-the-art forecasting systems, complete with common topics such as ML and deep learning (DL) as well as rarely touched-upon topics such as global forecasting models, cross-validation strategies, and forecast metrics. You’ll begin by exploring the basics of data handling, data visualization, and classical statistical methods before moving on to ML and DL models for time series forecasting. This book takes you on a hands-on journey in which you’ll develop state-of-the-art ML (linear regression to gradient-boosted trees) and DL (feed-forward neural networks, LSTMs, and transformers) models on a real-world dataset along with exploring practical topics such as interpretability. By the end of this book, you’ll be able to build world-class time series forecasting systems and tackle problems in the real world.What you will learn Find out how to manipulate and visualize time series data like a pro Set strong baselines with popular models such as ARIMA Discover how time series forecasting can be cast as regression Engineer features for machine learning models for forecasting Explore the exciting world of ensembling and stacking models Get to grips with the global forecasting paradigm Understand and apply state-of-the-art DL models such as N-BEATS and Autoformer Explore multi-step forecasting and cross-validation strategies Who this book is for The book is for data scientists, data analysts, machine learning engineers, and Python developers who want to build industry-ready time series models. Since the book explains most concepts from the ground up, basic proficiency in Python is all you need. Prior understanding of machine learning or forecasting will help speed up your learning. For experienced machine learning and forecasting practitioners, this book has a lot to offer in terms of advanced techniques and traversing the latest research frontiers in time series forecasting.
Inside Deep Learning
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Author : Edward Raff
language : en
Publisher: Simon and Schuster
Release Date : 2022-05-31
Inside Deep Learning written by Edward Raff and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems. In Inside Deep Learning, you will learn how to: Implement deep learning with PyTorch Select the right deep learning components Train and evaluate a deep learning model Fine tune deep learning models to maximize performance Understand deep learning terminology Adapt existing PyTorch code to solve new problems Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped--you'll dive into math, theory, and practical applications. Everything is clearly explained in plain English. About the Technology Deep learning doesn't have to be a black box! Knowing how your models and algorithms actually work gives you greater control over your results. And you don't have to be a mathematics expert or a senior data scientist to grasp what's going on inside a deep learning system. This book gives you the practical insight you need to understand and explain your work with confidence. About the Book Inside Deep Learning illuminates the inner workings of deep learning algorithms in a way that even machine learning novices can understand. You'll explore deep learning concepts and tools through plain language explanations, annotated code, and dozens of instantly useful PyTorch examples. Each type of neural network is clearly presented without complex math, and every solution in this book can run using readily available GPU hardware! What's Inside Select the right deep learning components Train and evaluate a deep learning model Fine tune deep learning models to maximize performance Understand deep learning terminology About the Reader For Python programmers with basic machine learning skills. About the Author Edward Raff is a Chief Scientist at Booz Allen Hamilton, and the author of the JSAT machine learning library. Quotes Pick up this book, and you won't be able to put it down. A rich, engaging knowledge base of deep learning math, algorithms, and models--just like the title says! - From the Foreword by Kirk Borne Ph.D., Chief Science Officer, DataPrime.ai The clearest and easiest book for learning deep learning principles and techniques I have ever read. The graphical representations for the algorithms are an eye-opening revelation. - Richard Vaughan, Purple Monkey Collective A great read for anyone interested in understanding the details of deep learning. - Vishwesh Ravi Shrimali, MBRDI.
Synthetic Data For Deep Learning
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Author : Sergey I. Nikolenko
language : en
Publisher: Springer Nature
Release Date : 2021-06-26
Synthetic Data For Deep Learning written by Sergey I. Nikolenko and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-26 with Computers categories.
This is the first book on synthetic data for deep learning, and its breadth of coverage may render this book as the default reference on synthetic data for years to come. The book can also serve as an introduction to several other important subfields of machine learning that are seldom touched upon in other books. Machine learning as a discipline would not be possible without the inner workings of optimization at hand. The book includes the necessary sinews of optimization though the crux of the discussion centers on the increasingly popular tool for training deep learning models, namely synthetic data. It is expected that the field of synthetic data will undergo exponential growth in the near future. This book serves as a comprehensive survey of the field. In the simplest case, synthetic data refers to computer-generated graphics used to train computer vision models. There are many more facets of synthetic data to consider. In the section on basic computer vision, the book discusses fundamental computer vision problems, both low-level (e.g., optical flow estimation) and high-level (e.g., object detection and semantic segmentation), synthetic environments and datasets for outdoor and urban scenes (autonomous driving), indoor scenes (indoor navigation), aerial navigation, and simulation environments for robotics. Additionally, it touches upon applications of synthetic data outside computer vision (in neural programming, bioinformatics, NLP, and more). It also surveys the work on improving synthetic data development and alternative ways to produce it such as GANs. The book introduces and reviews several different approaches to synthetic data in various domains of machine learning, most notably the following fields: domain adaptation for making synthetic data more realistic and/or adapting the models to be trained on synthetic data and differential privacy for generating synthetic data with privacy guarantees. This discussion is accompanied by an introduction into generative adversarial networks (GAN) and an introduction to differential privacy.
Deep Learning With Structured Data
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Author : Mark Ryan
language : en
Publisher: Simon and Schuster
Release Date : 2020-12-08
Deep Learning With Structured Data written by Mark Ryan and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-08 with Computers categories.
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
Comptia Cysa Cybersecurity Analyst Certification All In One Exam Guide Third Edition Exam Cs0 003
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Author : Mya Heath
language : en
Publisher: McGraw Hill Professional
Release Date : 2023-12-08
Comptia Cysa Cybersecurity Analyst Certification All In One Exam Guide Third Edition Exam Cs0 003 written by Mya Heath and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-08 with Computers categories.
Prepare for the CompTIA CySA+ certification exam using this fully updated self-study resource Take the current version of the challenging CompTIA CySA+TM certification exam with confidence using the detailed information contained in this up-to-date integrated study system. Based on proven pedagogy, the book contains detailed explanations, real-world examples, step-by-step exercises, and exam-focused special elements that teach and reinforce practical skills. CompTIA CySA+TM Cybersecurity Analyst Certification All-in-One Exam Guide, Third Edition (Exam CS0-003) covers 100% of 2023 exam objectives and features re-structured content and new topics. Online content enables you to test yourself with full-length, timed practice exams or create customized quizzes by chapter or exam domain. Designed to help you pass the exam with ease, this comprehensive guide also serves as an essential on-the-job reference. Includes access to the TotalTester Online test engine with 170 multiple-choice practice exam questions and additional performance-based questions Includes a 10% off exam voucher coupon, a $39 value Written by a team of recognized cybersecurity experts
Proceedings Of The First Nasa Dod Workshop On Evolvable Hardware
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Author : Adrian Stoica
language : en
Publisher: I E E E
Release Date : 1999
Proceedings Of The First Nasa Dod Workshop On Evolvable Hardware written by Adrian Stoica and has been published by I E E E this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Computers categories.
Looks at applying evolution-inspired formalisms to hardware design in order to produce self-reconfigurable electronic hardware by evolutionary and genetic reconfiguration mechanisms. The 35 papers cover the evolution of FPGAs, digital functions, analog and mixed- signal circuits, cellular automata and brain-inspired architectures; reconfiguration architectures and dynamic reconfiguration, advanced reconfigurable devices; applications to the design and adaptation of space sub-systems; and genetic algorithms applications. Titles, but not texts, are included for the nine invited talks. Authors, but not subjects, are indexed. Annotation copyrighted by Book News, Inc., Portland, OR.
Modern Deep Learning Foundation
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Author : Barak Or
language : en
Publisher: Independently Published
Release Date : 2025-08
Modern Deep Learning Foundation written by Barak Or and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08 with Computers categories.
This is not just another deep learning book. Modern Deep Learning Foundations is a complete, hands-on guide for building, training, and deploying neural networks - written specifically for engineers who care about real-world systems, not just theoretical results. Dr. Barak Or is an AI researcher, entrepreneur, and educator, with a PhD in ML for navigation systems, and a professional background that spans startups, deeptech technologies, and teaching at the Google-Reichman Tech School. He holds dual degrees in aeronautical engineering and economics & management from the Technion and has trained thousands of engineers across domains. What's Inside: Clear explanations of modern architectures: CNNs, RNNs, LSTMs, Transformers, Autoencoders, and more In-depth coverage of training essentials: loss functions, backpropagation, optimization (AdamW, Lion, Adafactor), mixed precision, and regularization Practical tools for industrial use: saving and versioning models, serving with FastAPI, and deploying to the cloud with full PyTorch examples Lessons on explainability (SHAP, Grad-CAM), transfer learning, tabular data, time series, and working with real-world constraints A closing roadmap for becoming a deep learning engineer who can ship systems Each lesson is concise - filled with illustrations, examples, and engineering principles designed to build real intuition. Bonus: This book also serves as the official companion to the ArtificialGate course platform, used by enterprise teams and academic programs worldwide. All content is designed to support learners across technical backgrounds, and available in multiple languages.
Machine Learning
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Author : Claude Sammut
language : en
Publisher: Morgan Kaufmann
Release Date : 2002
Machine Learning written by Claude Sammut and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Artificial intelligence categories.