Pro .NET Memory Management PDF Download
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Author: Konrad Kokosa
Publisher: Apress
ISBN: 1484240278
Category : Computers
Languages : en
Pages : 1091
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Book Description
Understand .NET memory management internal workings, pitfalls, and techniques in order to effectively avoid a wide range of performance and scalability problems in your software. Despite automatic memory management in .NET, there are many advantages to be found in understanding how .NET memory works and how you can best write software that interacts with it efficiently and effectively. Pro .NET Memory Management is your comprehensive guide to writing better software by understanding and working with memory management in .NET. Thoroughly vetted by the .NET Team at Microsoft, this book contains 25 valuable troubleshooting scenarios designed to help diagnose challenging memory problems. Readers will also benefit from a multitude of .NET memory management “rules” to live by that introduce methods for writing memory-aware code and the means for avoiding common, destructive pitfalls. What You'll LearnUnderstand the theoretical underpinnings of automatic memory management Take a deep dive into every aspect of .NET memory management, including detailed coverage of garbage collection (GC) implementation, that would otherwise take years of experience to acquire Get practical advice on how this knowledge can be applied in real-world software development Use practical knowledge of tools related to .NET memory management to diagnose various memory-related issuesExplore various aspects of advanced memory management, including use of Span and Memory types Who This Book Is For .NET developers, solution architects, and performance engineers
Author: Konrad Kokosa
Publisher: Apress
ISBN: 1484240278
Category : Computers
Languages : en
Pages : 1091
Get Book Here
Book Description
Understand .NET memory management internal workings, pitfalls, and techniques in order to effectively avoid a wide range of performance and scalability problems in your software. Despite automatic memory management in .NET, there are many advantages to be found in understanding how .NET memory works and how you can best write software that interacts with it efficiently and effectively. Pro .NET Memory Management is your comprehensive guide to writing better software by understanding and working with memory management in .NET. Thoroughly vetted by the .NET Team at Microsoft, this book contains 25 valuable troubleshooting scenarios designed to help diagnose challenging memory problems. Readers will also benefit from a multitude of .NET memory management “rules” to live by that introduce methods for writing memory-aware code and the means for avoiding common, destructive pitfalls. What You'll LearnUnderstand the theoretical underpinnings of automatic memory management Take a deep dive into every aspect of .NET memory management, including detailed coverage of garbage collection (GC) implementation, that would otherwise take years of experience to acquire Get practical advice on how this knowledge can be applied in real-world software development Use practical knowledge of tools related to .NET memory management to diagnose various memory-related issuesExplore various aspects of advanced memory management, including use of Span and Memory types Who This Book Is For .NET developers, solution architects, and performance engineers
Author: Patti Shank
Publisher: Createspace Independent Publishing Platform
ISBN: 9781545162408
Category : Communication in education
Languages : en
Pages : 0
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Book Description
The book examines 28 actionable tactics that you can use immediately to make your instruction easier to learn, remember, and apply. The tactics come from learning, information design, usability, and writing research and includes examples, checklists, and job aids.
Author: Peter Jones
Publisher: Walzone Press
ISBN:
Category : Computers
Languages : en
Pages : 217
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Book Description
Unlock the full potential of deep learning with "Efficient AI Solutions: Deploying Deep Learning with ONNX and CUDA", your comprehensive guide to deploying high-performance AI models across diverse environments. This expertly crafted book navigates the intricate landscape of deep learning deployment, offering in-depth coverage of the pivotal technologies ONNX and CUDA. From optimizing and preparing models for deployment to leveraging accelerated computing for real-time inference, this book equips you with the essential knowledge to bring your deep learning projects to life. Dive into the nuances of model interoperability with ONNX, understand the architecture of CUDA for parallel computing, and explore advanced optimization techniques to enhance model performance. Whether you're deploying to the cloud, edge devices, or mobile platforms, "Efficient AI Solutions: Deploying Deep Learning with ONNX and CUDA" provides strategic insights into cross-platform deployment, ensuring your models achieve broad accessibility and optimal performance. Designed for data scientists, machine learning engineers, and software developers, this resource assumes a foundational understanding of deep learning, guiding readers through a seamless transition from training to production. Troubleshoot with ease and adopt best practices to stay ahead of deployment challenges. Prepare for the future of deep learning deployment with a closer look at emerging trends and technologies shaping the field. Embrace the future of AI with "Efficient AI Solutions: Deploying Deep Learning with ONNX and CUDA" — your pathway to deploying efficient, scalable, and robust deep learning models.
Author: K. Venkata Murali Mohan
Publisher: CRC Press
ISBN: 104004591X
Category : Computers
Languages : en
Pages : 459
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Book Description
The 1st International Conference on Disruptive Technologies in Computing and Communication Systems (ICDTCCS - 2023) has received overwhelming response on call for papers and over 119 papers from all over globe were received. We must appreciate the untiring contribution of the members of the organizing committee and Reviewers Board who worked hard to review the papers and finally a set of 69 technical papers were recommended for publication in the conference proceedings. We are grateful to the Chief Guest Prof Atul Negi, Dean – Hyderabad Central University, Guest of Honor Justice John S Spears -Professor University of West Los Angeles CA, and Keynote Speakers Prof A. Govardhan, Rector JNTU H, Prof A.V.Ramana Registrar – S.K.University, Dr Tara Bedi Trinity College Dublin, Prof C.R.Rao – Professor University of Hyderabad, Mr Peddigari Bala, Chief Innovation Officer TCS, for kindly accepting the invitation to deliver the valuable speech and keynote address in the same. We would like to convey our gratitude to Prof D. Asha Devi - SNIST, Dr B.Deevena Raju – ICFAI University, Dr Nekuri Naveen - HCU, Dr A.Mahesh Babu - KLH, Dr K.Hari Priya – Anurag University and Prof Kameswara Rao –SRK Bhimavaram for giving consent as session Chair. We are also thankful to our Chairman Sri Teegala Krishna Reddy, Secretary Dr. T.Harinath Reddy and Sri T. Amarnath Reddy for providing funds to organize the conference. We are also thankful to the contributors whose active interest and participation to ICDTCCS - 2023 has made the conference a glorious success. Finally, so many people have extended their helping hands in many ways for organizing the conference successfully. We are especially thankful to them.
Author: Guglielmo Iozzia
Publisher: Packt Publishing Ltd
ISBN: 1788999703
Category : Computers
Languages : en
Pages : 310
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Book Description
Speed up the design and implementation of deep learning solutions using Apache Spark Key FeaturesExplore the world of distributed deep learning with Apache SparkTrain neural networks with deep learning libraries such as BigDL and TensorFlowDevelop Spark deep learning applications to intelligently handle large and complex datasetsBook Description Deep learning is a subset of machine learning where datasets with several layers of complexity can be processed. Hands-On Deep Learning with Apache Spark addresses the sheer complexity of technical and analytical parts and the speed at which deep learning solutions can be implemented on Apache Spark. The book starts with the fundamentals of Apache Spark and deep learning. You will set up Spark for deep learning, learn principles of distributed modeling, and understand different types of neural nets. You will then implement deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) on Spark. As you progress through the book, you will gain hands-on experience of what it takes to understand the complex datasets you are dealing with. During the course of this book, you will use popular deep learning frameworks, such as TensorFlow, Deeplearning4j, and Keras to train your distributed models. By the end of this book, you'll have gained experience with the implementation of your models on a variety of use cases. What you will learnUnderstand the basics of deep learningSet up Apache Spark for deep learningUnderstand the principles of distribution modeling and different types of neural networksObtain an understanding of deep learning algorithmsDiscover textual analysis and deep learning with SparkUse popular deep learning frameworks, such as Deeplearning4j, TensorFlow, and KerasExplore popular deep learning algorithms Who this book is for If you are a Scala developer, data scientist, or data analyst who wants to learn how to use Spark for implementing efficient deep learning models, Hands-On Deep Learning with Apache Spark is for you. Knowledge of the core machine learning concepts and some exposure to Spark will be helpful.
Author: Suneeta Mall
Publisher: "O'Reilly Media, Inc."
ISBN: 1098145240
Category : Computers
Languages : en
Pages : 404
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Book Description
Bringing a deep-learning project into production at scale is quite challenging. To successfully scale your project, a foundational understanding of full stack deep learning, including the knowledge that lies at the intersection of hardware, software, data, and algorithms, is required. This book illustrates complex concepts of full stack deep learning and reinforces them through hands-on exercises to arm you with tools and techniques to scale your project. A scaling effort is only beneficial when it's effective and efficient. To that end, this guide explains the intricate concepts and techniques that will help you scale effectively and efficiently. You'll gain a thorough understanding of: How data flows through the deep-learning network and the role the computation graphs play in building your model How accelerated computing speeds up your training and how best you can utilize the resources at your disposal How to train your model using distributed training paradigms, i.e., data, model, and pipeline parallelism How to leverage PyTorch ecosystems in conjunction with NVIDIA libraries and Triton to scale your model training Debugging, monitoring, and investigating the undesirable bottlenecks that slow down your model training How to expedite the training lifecycle and streamline your feedback loop to iterate model development A set of data tricks and techniques and how to apply them to scale your training model How to select the right tools and techniques for your deep-learning project Options for managing the compute infrastructure when running at scale
Author: Management Association, Information Resources
Publisher: IGI Global
ISBN: 1799804151
Category : Computers
Languages : en
Pages : 1707
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Book Description
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.
Author: Andres Rodriguez
Publisher: Morgan & Claypool Publishers
ISBN: 1681739674
Category : Computers
Languages : en
Pages : 267
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Book Description
This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications. The exponential growth in computational power is slowing at a time when the amount of compute consumed by state-of-the-art deep learning (DL) workloads is rapidly growing. Model size, serving latency, and power constraints are a significant challenge in the deployment of DL models for many applications. Therefore, it is imperative to codesign algorithms, compilers, and hardware to accelerate advances in this field with holistic system-level and algorithm solutions that improve performance, power, and efficiency. Advancing DL systems generally involves three types of engineers: (1) data scientists that utilize and develop DL algorithms in partnership with domain experts, such as medical, economic, or climate scientists; (2) hardware designers that develop specialized hardware to accelerate the components in the DL models; and (3) performance and compiler engineers that optimize software to run more efficiently on a given hardware. Hardware engineers should be aware of the characteristics and components of production and academic models likely to be adopted by industry to guide design decisions impacting future hardware. Data scientists should be aware of deployment platform constraints when designing models. Performance engineers should support optimizations across diverse models, libraries, and hardware targets. The purpose of this book is to provide a solid understanding of (1) the design, training, and applications of DL algorithms in industry; (2) the compiler techniques to map deep learning code to hardware targets; and (3) the critical hardware features that accelerate DL systems. This book aims to facilitate co-innovation for the advancement of DL systems. It is written for engineers working in one or more of these areas who seek to understand the entire system stack in order to better collaborate with engineers working in other parts of the system stack. The book details advancements and adoption of DL models in industry, explains the training and deployment process, describes the essential hardware architectural features needed for today's and future models, and details advances in DL compilers to efficiently execute algorithms across various hardware targets. Unique in this book is the holistic exposition of the entire DL system stack, the emphasis on commercial applications, and the practical techniques to design models and accelerate their performance. The author is fortunate to work with hardware, software, data scientist, and research teams across many high-technology companies with hyperscale data centers. These companies employ many of the examples and methods provided throughout the book.
Author: Karthik, S.
Publisher: IGI Global
ISBN: 1522530169
Category : Computers
Languages : en
Pages : 287
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Book Description
The expansion of digital data has transformed various sectors of business such as healthcare, industrial manufacturing, and transportation. A new way of solving business problems has emerged through the use of machine learning techniques in conjunction with big data analytics. Deep Learning Innovations and Their Convergence With Big Data is a pivotal reference for the latest scholarly research on upcoming trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. Featuring extensive coverage on a broad range of topics and perspectives such as deep neural network, domain adaptation modeling, and threat detection, this book is ideally designed for researchers, professionals, and students seeking current research on the latest trends in the field of deep learning techniques in big data analytics.
Author:
Publisher: Academic Press
ISBN: 0128231246
Category : Mathematics
Languages : en
Pages : 416
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Book Description
Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into arti?cial Intelligence and the growth it has seen with the advent of Deep Neural Networks (DNNs) and Machine Learning. Updates in this release include chapters on Hardware accelerator systems for artificial intelligence and machine learning, Introduction to Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Deep Learning with GPUs, Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures, Architecture of NPU for DNN, Hardware Architecture for Convolutional Neural Network for Image Processing, FPGA based Neural Network Accelerators, and much more. - Updates on new information on the architecture of GPU, NPU and DNN - Discusses In-memory computing, Machine intelligence and Quantum computing - Includes sections on Hardware Accelerator Systems to improve processing efficiency and performance
