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Author : 黃漢威
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (134 download)
Download or read book Enhancing Random Shuffling Efficiency for Machine Learning for Systems with Nonvolatile Memory Storage written by 黃漢威 and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Yue Zhu
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (134 download)
Download or read book User-Level I/O Accelerations for High-Performance Deep Learning Applications written by Yue Zhu and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the popularity of microprocessors and scale-out system architectures, many large-scale high-performance computing (HPC) systems are built from a collection of compute servers, with an identical set of resources such as CPU, memory, and storage. A variety of applications have been leveraging the tremendous computation capacity on these large-scale HPC systems. Scientific applications and deep learning (DL) training are two of the popular workloads on HPC systems. However, with the rapid growth of the computation power, it has also become increasingly important to fill in the computation and I/O performance gap for these applications and workloads on HPC systems. In recent years, many research efforts have been made to explore user-level file systems on HPC systems for various workloads due to the flexibility of implementation and maintenance in user space. In particular, scientific applications which have two typical I/O patterns (checkpoint/restart and multi-dimensional I/O) have been able to utilize different specialized user-level file systems in a single job. However, non-trivial overheads can be introduced in such a method. We need to carefully review the overheads in order to mit- igate the performance degradation. In addition, the existing methods of using user-level file systems are not sufficient to meet the fundamental I/O needs of large-scale DL training on HPC systems. Firstly, in DL training, random samples are organized into batches to update model parameters in iterations. This is to avoid the model being biased by the input sequences' noise, which allows faster convergence speed and reduces memory consumption during the training computation. This results in massive random reads for data shuffling across the entire datasets on storage systems. Such a random read I/O pattern is significantly different from the traditional scientific workloads. Moreover, leadership HPC systems are often equipped with a large pool of burst buffers in the form of flash or non-volatile memory (NVM) devices. DL applica- tions running on these systems face the resource underutilization problem. This is because NVM devices' performance with respect to low latency and high bandwidth can be severely underutilized under heavy CPU and memory workloads. In this environment, the flash or NVMe storage devices are capable of low-latency and high-bandwidth I/O services, but the complex software stack significantly hampers such capabilities for I/O processing in the kernel. Also, due to DL training accuracy and performance concerns, the storage capacity and the performance of on-node storage devices on the nodes allocated to the training job are not sufficient to store an entire dataset and match the training speed, respectively.This dissertation focus on applying user-level file systems on HPC systems. Our overarching goal is to accelerate the I/O supports on HPC systems through specialized user-level file systems for popular workloads. In specific, we want to bring lightweight user-level file systems as efficient intermediates to reduce the performance overhead and ease the use of multiple FUSE file systems in a single job, orchestrate the data movement between storage tiers and DL applications, and improve the storage resource utilization for a pool of NVMe SSDs in DL training. Based on these design goals, we investigate the issues and challenges when applying existing user-level file systems to the popular workloads, then propose three strategies to meet our goals. Firstly, we have studied the problem of excessive cost in crossing the user-kernel boundary when using multiple traditional user-level file systems, and we design Direct-FUSE to support multiple FUSE file sys- tems as well as other, custom user-level file systems in user space without the need to cross the user/kernel boundary into the FUSE kernel module. All layers of Direct-FUSE are in user space, and applications can directly use pre-defined unified file system calls to interact with different user-defined file systems. Our performance results show that Direct-FUSE can outperform some native FUSE file systems and does not add significant overhead over backend file systems. Secondly, we examine the I/O patterns of deep neural networks and study the performance overheads when loading samples from some popular DL applications. Then, we introduce an entropy-aware I/O framework called DeepIO for large-scale deep learning on HPC systems. It coordinates the use of memory, communication, and I/O resources for efficient training of datasets. DeepIO features an I/O pipeline that utilizes several novel optimizations: RDMA (Remote Direct Memory Access)-assisted in-situ shuffling, input pipelining, and entropy-aware opportunistic ordering. It outperforms the state-of-the-art persistent memory based distributed file systems for efficient sample load- ing during DL training. Thirdly, besides examining the I/O patterns of deep neural networks, we also reveal a critical need of loading many small samples randomly and the issues of storage resources underutilization for successful training. Based on these understandings, we design a specialized Deep Learning File System (DLFS) with an in-memory tree-based sample directory for metadata management and user-level storage disaggregation through the SPDK protocol. Our experimental results show that DLFS can dramatically improve the throughput of training for deep neural networks when compared with the kernel-based local Ext4 file system. Furthermore, DLFS demonstrates its capability of achieving efficient user-level storage disaggregation with very little CPU utilization. In conclusion, the first branch concentrates on enriching the functionality and enhancing the performance of the Direct-FUSE framework; the second and third branches focus on wisely storing and prefetching datasets with the coordination of hierarchical storage tiers and fast interconnect, respectively. By exploring these three branches, we can further accelerate the specialized user-level file systems for popular workloads on HPC systems.
Author : Giuseppe Bonaccorso
Publisher : Packt Publishing Ltd
ISBN 13 : 1785884514
Total Pages : 360 pages
Book Rating : 4.7/5 (858 download)
Download or read book Machine Learning Algorithms written by Giuseppe Bonaccorso and published by Packt Publishing Ltd. This book was released on 2017-07-24 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation. Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide. Who This Book Is For This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here. What You Will Learn Acquaint yourself with important elements of Machine Learning Understand the feature selection and feature engineering process Assess performance and error trade-offs for Linear Regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector machines Implement clusters to a dataset Explore the concept of Natural Processing Language and Recommendation Systems Create a ML architecture from scratch. In Detail As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously. On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem. Style and approach An easy-to-follow, step-by-step guide that will help you get to grips with real -world applications of Algorithms for Machine Learning.
Author : Vivienne Sze
Publisher : Springer Nature
ISBN 13 : 3031017668
Total Pages : 254 pages
Book Rating : 4.0/5 (31 download)
Download or read book Efficient Processing of Deep Neural Networks written by Vivienne Sze and published by Springer Nature. This book was released on 2022-05-31 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.
Author : Francois Chollet
Publisher : Simon and Schuster
ISBN 13 : 1638352046
Total Pages : 597 pages
Book Rating : 4.6/5 (383 download)
Download or read book Deep Learning with Python written by Francois Chollet and published by Simon and Schuster. This book was released on 2017-11-30 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance
Author : Brett Lantz
Publisher : Packt Publishing Ltd
ISBN 13 : 1782162151
Total Pages : 587 pages
Book Rating : 4.7/5 (821 download)
Download or read book Machine Learning with R written by Brett Lantz and published by Packt Publishing Ltd. This book was released on 2013-10-25 with total page 587 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.
Author : Frank Kane
Publisher : Packt Publishing Ltd
ISBN 13 : 1787280225
Total Pages : 415 pages
Book Rating : 4.7/5 (872 download)
Download or read book Hands-On Data Science and Python Machine Learning written by Frank Kane and published by Packt Publishing Ltd. This book was released on 2017-07-31 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale machine learning using Apache Spark. About This Book Take your first steps in the world of data science by understanding the tools and techniques of data analysis Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods Learn how to use Apache Spark for processing Big Data efficiently Who This Book Is For If you are a budding data scientist or a data analyst who wants to analyze and gain actionable insights from data using Python, this book is for you. Programmers with some experience in Python who want to enter the lucrative world of Data Science will also find this book to be very useful, but you don't need to be an expert Python coder or mathematician to get the most from this book. What You Will Learn Learn how to clean your data and ready it for analysis Implement the popular clustering and regression methods in Python Train efficient machine learning models using decision trees and random forests Visualize the results of your analysis using Python's Matplotlib library Use Apache Spark's MLlib package to perform machine learning on large datasets In Detail Join Frank Kane, who worked on Amazon and IMDb's machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank's successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis. Style and approach This comprehensive book is a perfect blend of theory and hands-on code examples in Python which can be used for your reference at any time.
Author : Jeffrey Scott Vitter
Publisher : Now Publishers Inc
ISBN 13 : 1601981066
Total Pages : 192 pages
Book Rating : 4.6/5 (19 download)
Download or read book Algorithms and Data Structures for External Memory written by Jeffrey Scott Vitter and published by Now Publishers Inc. This book was released on 2008 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: Describes several useful paradigms for the design and implementation of efficient external memory (EM) algorithms and data structures. The problem domains considered include sorting, permuting, FFT, scientific computing, computational geometry, graphs, databases, geographic information systems, and text and string processing.
Author : Rio Yokota
Publisher : Springer
ISBN 13 : 3319699539
Total Pages : 301 pages
Book Rating : 4.3/5 (196 download)
Download or read book Supercomputing Frontiers written by Rio Yokota and published by Springer. This book was released on 2018-03-20 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: It constitutes the refereed proceedings of the 4th Asian Supercomputing Conference, SCFA 2018, held in Singapore in March 2018. Supercomputing Frontiers will be rebranded as Supercomputing Frontiers Asia (SCFA), which serves as the technical programme for SCA18. The technical programme for SCA18 consists of four tracks: Application, Algorithms & Libraries Programming System Software Architecture, Network/Communications & Management Data, Storage & Visualisation The 20 papers presented in this volume were carefully reviewed nd selected from 60 submissions.
Author : Kuan-Ching Li
Publisher : CRC Press
ISBN 13 : 1498768083
Total Pages : 489 pages
Book Rating : 4.4/5 (987 download)
Download or read book Big Data Management and Processing written by Kuan-Ching Li and published by CRC Press. This book was released on 2017-05-19 with total page 489 pages. Available in PDF, EPUB and Kindle. Book excerpt: From the Foreword: "Big Data Management and Processing is [a] state-of-the-art book that deals with a wide range of topical themes in the field of Big Data. The book, which probes many issues related to this exciting and rapidly growing field, covers processing, management, analytics, and applications... [It] is a very valuable addition to the literature. It will serve as a source of up-to-date research in this continuously developing area. The book also provides an opportunity for researchers to explore the use of advanced computing technologies and their impact on enhancing our capabilities to conduct more sophisticated studies." ---Sartaj Sahni, University of Florida, USA "Big Data Management and Processing covers the latest Big Data research results in processing, analytics, management and applications. Both fundamental insights and representative applications are provided. This book is a timely and valuable resource for students, researchers and seasoned practitioners in Big Data fields. --Hai Jin, Huazhong University of Science and Technology, China Big Data Management and Processing explores a range of big data related issues and their impact on the design of new computing systems. The twenty-one chapters were carefully selected and feature contributions from several outstanding researchers. The book endeavors to strike a balance between theoretical and practical coverage of innovative problem solving techniques for a range of platforms. It serves as a repository of paradigms, technologies, and applications that target different facets of big data computing systems. The first part of the book explores energy and resource management issues, as well as legal compliance and quality management for Big Data. It covers In-Memory computing and In-Memory data grids, as well as co-scheduling for high performance computing applications. The second part of the book includes comprehensive coverage of Hadoop and Spark, along with security, privacy, and trust challenges and solutions. The latter part of the book covers mining and clustering in Big Data, and includes applications in genomics, hospital big data processing, and vehicular cloud computing. The book also analyzes funding for Big Data projects.
Author : Bill Chambers
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491912294
Total Pages : 594 pages
Book Rating : 4.4/5 (919 download)
Download or read book Spark: The Definitive Guide written by Bill Chambers and published by "O'Reilly Media, Inc.". This book was released on 2018-02-08 with total page 594 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. Youâ??ll explore the basic operations and common functions of Sparkâ??s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Sparkâ??s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasetsâ??Sparkâ??s core APIsâ??through worked examples Dive into Sparkâ??s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Sparkâ??s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation
Author : Joanne Quinn
Publisher : Corwin Press
ISBN 13 : 1544385404
Total Pages : 297 pages
Book Rating : 4.5/5 (443 download)
Download or read book Dive Into Deep Learning written by Joanne Quinn and published by Corwin Press. This book was released on 2019-07-15 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Mary Scannell
Publisher : McGraw Hill Professional
ISBN 13 : 0071743669
Total Pages : 240 pages
Book Rating : 4.0/5 (717 download)
Download or read book The Big Book of Conflict Resolution Games: Quick, Effective Activities to Improve Communication, Trust and Collaboration written by Mary Scannell and published by McGraw Hill Professional. This book was released on 2010-05-28 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Make workplace conflict resolution a game that EVERYBODY wins! Recent studies show that typical managers devote more than a quarter of their time to resolving coworker disputes. The Big Book of Conflict-Resolution Games offers a wealth of activities and exercises for groups of any size that let you manage your business (instead of managing personalities). Part of the acclaimed, bestselling Big Books series, this guide offers step-by-step directions and customizable tools that empower you to heal rifts arising from ineffective communication, cultural/personality clashes, and other specific problem areas—before they affect your organization's bottom line. Let The Big Book of Conflict-Resolution Games help you to: Build trust Foster morale Improve processes Overcome diversity issues And more Dozens of physical and verbal activities help create a safe environment for teams to explore several common forms of conflict—and their resolution. Inexpensive, easy-to-implement, and proved effective at Fortune 500 corporations and mom-and-pop businesses alike, the exercises in The Big Book of Conflict-Resolution Games delivers everything you need to make your workplace more efficient, effective, and engaged.
Author : Richard W. Pew
Publisher : Worth
ISBN 13 : 9781464173950
Total Pages : 0 pages
Book Rating : 4.1/5 (739 download)
Download or read book Psychology and the Real World written by Richard W. Pew and published by Worth. This book was released on 2019-02-24 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Psychology and the Real World: Essays Illustrating Fundamental Contributions to Society is a collection of brief, personal, original essays, ranging in length from 2500 to 3500 words, in which leading academic psychologists describe what their area of research has contributed to society. The authors are true stars in the field of psychology. Some of their work (for example, Elizabeth Loftus’s studies of false memories, Paul Ekman’s research on facial expression, and Eliot Aronson’s “jigsaw,” or cooperative, classroom studies) is well known to the public. The research of others is less familiar to nonspecialists, but no less fascinating. The book is unique the world of textbook ancillaries in that it does not reprint writings. Rather, innovative psychological scientists clearly and entertainingly tell readers why their research matters and how their line of inquiry developed. The concept for the book came from the FABBS Foundation, a nonprofit educational foundation that supports the work of 22 scholarly societies that span the cognitive, psychological, behavioral, and brain sciences. The authors have volunteered their contributions. These authors have agreed that all grants, advances, and royalties and other financial earnings from this volume will go to the FABBS Foundation to support their educational mission.
Author : Giuseppe Nicosia
Publisher : Springer Nature
ISBN 13 : 3030375994
Total Pages : 798 pages
Book Rating : 4.0/5 (33 download)
Download or read book Machine Learning, Optimization, and Data Science written by Giuseppe Nicosia and published by Springer Nature. This book was released on 2020-01-03 with total page 798 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the post-conference proceedings of the 5th International Conference on Machine Learning, Optimization, and Data Science, LOD 2019, held in Siena, Italy, in September 2019. The 54 full papers presented were carefully reviewed and selected from 158 submissions. The papers cover topics in the field of machine learning, artificial intelligence, reinforcement learning, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications.
Author : Clarence Chio
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491979852
Total Pages : 394 pages
Book Rating : 4.4/5 (919 download)
Download or read book Machine Learning and Security written by Clarence Chio and published by "O'Reilly Media, Inc.". This book was released on 2018-01-26 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you’ll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis. Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike. Learn how machine learning has contributed to the success of modern spam filters Quickly detect anomalies, including breaches, fraud, and impending system failure Conduct malware analysis by extracting useful information from computer binaries Uncover attackers within the network by finding patterns inside datasets Examine how attackers exploit consumer-facing websites and app functionality Translate your machine learning algorithms from the lab to production Understand the threat attackers pose to machine learning solutions
Author : Abhishek Thakur
Publisher : Abhishek Thakur
ISBN 13 : 8269211508
Total Pages : 300 pages
Book Rating : 4.2/5 (692 download)
Download or read book Approaching (Almost) Any Machine Learning Problem written by Abhishek Thakur and published by Abhishek Thakur. This book was released on 2020-07-04 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is not a traditional book. The book has a lot of code. If you don't like the code first approach do not buy this book. Making code available on Github is not an option. This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book doesn't explain the algorithms but is more oriented towards how and what should you use to solve machine learning and deep learning problems. The book is not for you if you are looking for pure basics. The book is for you if you are looking for guidance on approaching machine learning problems. The book is best enjoyed with a cup of coffee and a laptop/workstation where you can code along. Table of contents: - Setting up your working environment - Supervised vs unsupervised learning - Cross-validation - Evaluation metrics - Arranging machine learning projects - Approaching categorical variables - Feature engineering - Feature selection - Hyperparameter optimization - Approaching image classification & segmentation - Approaching text classification/regression - Approaching ensembling and stacking - Approaching reproducible code & model serving There are no sub-headings. Important terms are written in bold. I will be answering all your queries related to the book and will be making YouTube tutorials to cover what has not been discussed in the book. To ask questions/doubts, visit this link: https://bit.ly/aamlquestions And Subscribe to my youtube channel: https://bit.ly/abhitubesub
