Deep Learning Deployment With Onnx And Cuda
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Efficient Ai Solutions Deploying Deep Learning With Onnx And Cuda
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Author : Peter Jones
language : en
Publisher: Walzone Press
Release Date : 2025-01-12
Efficient Ai Solutions Deploying Deep Learning With Onnx And Cuda written by Peter Jones and has been published by Walzone Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-12 with Computers categories.
Dive into the world of containers with "Mastering Docker Containers: From Development to Deployment," your comprehensive guide to mastering Docker, the revolutionary technology that has reshaped software development and deployment. This expertly crafted book is designed for developers, DevOps professionals, and systems administrators who are familiar with the basics of Docker and looking to elevate their skills to the next level. Spanning from foundational concepts to complex advanced topics, this book covers the entire spectrum of Docker functionalities and best practices. Explore chapters dedicated to image creation, optimization, networking, data management, security, debugging, monitoring, and the pivotal role of Docker in Continuous Integration and Continuous Deployment (CI/CD) processes. Each chapter is meticulously structured to provide in-depth knowledge, practical tips, and best practices, ensuring you gain a comprehensive understanding of Docker's capabilities and how to leverage them in real-world scenarios. Whether you aim to optimize your development workflows, secure your containerized applications, or implement scalable CI/CD pipelines, this book provides the insights and guidance needed to achieve proficiency in Docker operations. Empower yourself to efficiently manage and deploy containerized applications with confidence. 'Mastering Docker Containers: From Development to Deployment' is the essential resource for professionals seeking to harness the full potential of Docker in modern software environments.
Deep Learning Deployment With Onnx And Cuda
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Author : Nate Phoetean
language : en
Publisher: Independently Published
Release Date : 2024-04-05
Deep Learning Deployment With Onnx And Cuda written by Nate Phoetean and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-05 with Computers categories.
Unlock the full potential of deep learning with "Deep Learning Deployment 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, "Deep Learning Deployment 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 "Deep Learning Deployment with ONNX and CUDA" - your pathway to deploying efficient, scalable, and robust deep learning models.
Learning Pytorch 2 0
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Author : Matthew Rosch
language : en
Publisher: GitforGits
Release Date : 2023-07-01
Learning Pytorch 2 0 written by Matthew Rosch and has been published by GitforGits this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-01 with Computers categories.
This book is a comprehensive guide to understanding and utilizing PyTorch 2.0 for deep learning applications. It starts with an introduction to PyTorch, its various advantages over other deep learning frameworks, and its blend with CUDA for GPU acceleration. We delve into the heart of PyTorch – tensors, learning their different types, properties, and operations. Through step-by-step examples, the reader learns to perform basic arithmetic operations on tensors, manipulate them, and understand errors related to tensor shapes. A substantial portion of the book is dedicated to illustrating how to build simple PyTorch models. This includes uploading and preparing datasets, defining the architecture, training, and predicting. It provides hands-on exercises with a real-world dataset. The book then dives into exploring PyTorch's nn module and gives a detailed comparison of different types of networks like Feedforward, RNN, GRU, CNN, and their combination. Further, the book delves into understanding the training process and PyTorch's optim module. It explores the overview of optimization algorithms like Gradient Descent, SGD, Mini-batch Gradient Descent, Momentum, Adagrad, and Adam. A separate chapter focuses on advanced concepts in PyTorch 2.0, like model serialization, optimization, distributed training, and PyTorch Quantization API. In the final chapters, the book discusses the differences between TensorFlow 2.0 and PyTorch 2.0 and the step-by-step process of migrating a TensorFlow model to PyTorch 2.0 using ONNX. It provides an overview of common issues encountered during this process and how to resolve them. Key Learnings A comprehensive introduction to PyTorch and CUDA for deep learning. Detailed understanding and operations on PyTorch tensors. Step-by-step guide to building simple PyTorch models. Insight into PyTorch's nn module and comparison of various network types. Overview of the training process and exploration of PyTorch's optim module. Understanding advanced concepts in PyTorch like model serialization and optimization. Knowledge of distributed training in PyTorch. Practical guide to using PyTorch's Quantization API. Differences between TensorFlow 2.0 and PyTorch 2.0. Guidance on migrating TensorFlow models to PyTorch using ONNX. Table of Content Introduction to Pytorch 2.0 and CUDA 11.8 Getting Started with Tensors Advanced Tensors Operations Building Neural Networks with PyTorch 2.0 Training Neural Networks in PyTorch 2.0 PyTorch 2.0 Advanced Migrating from TensorFlow to PyTorch 2.0 End-to-End PyTorch Regression Model Audience A perfect and skillful book for every machine learning engineer, data scientist, AI engineer and data researcher who are passionately looking towards drawing actionable intelligence using PyTorch 2.0. Knowing Python and the basics of deep learning is all you need to sail through this book.
Learning Pytorch 2 0 Second Edition
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Author : Matthew Rosch
language : en
Publisher: GitforGits
Release Date : 2024-10-05
Learning Pytorch 2 0 Second Edition written by Matthew Rosch and has been published by GitforGits this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-05 with Computers categories.
"Learning PyTorch 2.0, Second Edition" is a fast-learning, hands-on book that emphasizes practical PyTorch scripting and efficient model development using PyTorch 2.3 and CUDA 12. This edition is centered on practical applications and presents a concise methodology for attaining proficiency in the most recent features of PyTorch. The book presents a practical program based on the fish dataset which provides step-by-step guidance through the processes of building, training and deploying neural networks, with each example prepared for immediate implementation. Given your familiarity with machine learning and neural networks, this book offers concise explanations of foundational topics, allowing you to proceed directly to the practical, advanced aspects of PyTorch programming. The key learnings include the design of various types of neural networks, the use of torch.compile() for performance optimization, the deployment of models using TorchServe, and the implementation of quantization for efficient inference. Furthermore, you will also learn to migrate TensorFlow models to PyTorch using the ONNX format. The book employs essential libraries, including torchvision, torchserve, tf2onnx, onnxruntime, and requests, to facilitate seamless integration of PyTorch with production environments. Regardless of whether the objective is to fine-tune models or to deploy them on a large scale, this second edition is designed to ensure maximum efficiency and speed, with practical PyTorch scripting at the forefront of each chapter. Key Learnings Master tensor manipulations and advanced operations using PyTorch's efficient tensor libraries. Build feedforward, convolutional, and recurrent neural networks from scratch. Implement transformer models for modern natural language processing tasks. Use CUDA 12 and mixed precision training (AMP) to accelerate model training and inference. Deploy PyTorch models in production using TorchServe, including multi-model serving and versioning. Migrate TensorFlow models to PyTorch using ONNX format for seamless cross-framework compatibility. Optimize neural network architectures using torch.compile() for improved speed and efficiency. Utilize PyTorch's Quantization API to reduce model size and speed up inference. Setup custom layers and architectures for neural networks to tackle domain-specific problems. Monitor and log model performance in real-time using TorchServe's built-in tools and configurations. Table of Content Introduction To PyTorch 2.3 and CUDA 12 Getting Started with Tensors Building Neural Networks with PyTorch Training Neural Networks Advanced Neural Network Architectures Quantization and Model Optimization Migrating TensorFlow to PyTorch Deploying PyTorch Models with TorchServe
Data Science Quick Reference Manual Deep Learning
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Author : Mario A. B. Capurso
language : en
Publisher: Mario Capurso
Release Date :
Data Science Quick Reference Manual Deep Learning written by Mario A. B. Capurso and has been published by Mario Capurso this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
This work follows the 2021 curriculum of the Association for Computing Machinery for specialists in Data Sciences, with the aim of producing a manual that collects notions in a simplified form, facilitating a personal training path starting from specialized skills in Computer Science or Mathematics or Statistics. It has a bibliography with links to quality material but freely usable for your own training and contextual practical exercises. Part in a series of texts, it first summarizes the standard CRISP DM working methodology used in this work and in Data Science projects. As this text uses Orange for the application aspects, it describes its installation and widgets. The data modeling phase is considered from the perspective of machine learning by summarizing machine learning types, model types, problem types, and algorithm types. Deep Learning techniques are described considering the architectures of the Perceptron, Neocognitron, the neuron with Backpropagation and the activation functions, the Feed Forward Networks, the Autoencoders, the recurrent networks and the LSTM and GRU, the Transformer Neural Networks, the Convolutional Neural Networks and Generative Adversarial Networks and analyzed the building blocks. Regularization techniques (Dropout, Early stopping and others), visual design and simulation techniques and tools, the most used algorithms and the best known architectures (LeNet, VGGnet, ResNet, Inception and others) are considered, closing with a set of practical tips and tricks. The exercises are described with Orange and Python using the Keras/Tensorflow library. The text is accompanied by supporting material and it is possible to download the examples and the test data.
Production Ready Applied Deep Learning
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Author : Tomasz Palczewski
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-08-30
Production Ready Applied Deep Learning written by Tomasz Palczewski 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 2022-08-30 with Computers categories.
Supercharge your skills for developing powerful deep learning models and distributing them at scale efficiently using cloud services Key Features Understand how to execute a deep learning project effectively using various tools available Learn how to develop PyTorch and TensorFlow models at scale using Amazon Web Services Explore effective solutions to various difficulties that arise from model deployment Book Description Machine learning engineers, deep learning specialists, and data engineers encounter various problems when moving deep learning models to a production environment. The main objective of this book is to close the gap between theory and applications by providing a thorough explanation of how to transform various models for deployment and efficiently distribute them with a full understanding of the alternatives. First, you will learn how to construct complex deep learning models in PyTorch and TensorFlow. Next, you will acquire the knowledge you need to transform your models from one framework to the other and learn how to tailor them for specific requirements that deployment environments introduce. The book also provides concrete implementations and associated methodologies that will help you apply the knowledge you gain right away. You will get hands-on experience with commonly used deep learning frameworks and popular cloud services designed for data analytics at scale. Additionally, you will get to grips with the authors' collective knowledge of deploying hundreds of AI-based services at a large scale. By the end of this book, you will have understood how to convert a model developed for proof of concept into a production-ready application optimized for a particular production setting. What you will learn Understand how to develop a deep learning model using PyTorch and TensorFlow Convert a proof-of-concept model into a production-ready application Discover how to set up a deep learning pipeline in an efficient way using AWS Explore different ways to compress a model for various deployment requirements Develop Android and iOS applications that run deep learning on mobile devices Monitor a system with a deep learning model in production Choose the right system architecture for developing and deploying a model Who this book is for Machine learning engineers, deep learning specialists, and data scientists will find this book helpful in closing the gap between the theory and application with detailed examples. Beginner-level knowledge in machine learning or software engineering will help you grasp the concepts covered in this book easily.
Artificial Intelligence Research And Development
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Author : I. Sanz
language : en
Publisher: IOS Press
Release Date : 2023-11-09
Artificial Intelligence Research And Development written by I. Sanz and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-09 with Computers categories.
Artificial intelligence is no longer solely the preserve of computer scientists and researchers; it is now a part of all our lives, and hardly a day goes by without discussion and debate about the implications of its many applications in the mainstream media. This book presents the proceedings of CCIA 2023, the 25th International Conference of the Catalan Association for Artificial Intelligence, held from 25 - 27 October 2023 in Barcelona, Spain. CCIA serves as an annual forum welcoming participants from around the globe. The theme of the 2023 conference was Supportive AI, the main goals of which are to strengthen collaboration between research and industry by sharing the latest advances in artificial intelligence, and opening discussion about how AI can better support the current needs of industry. A total of 54 submissions were received for the conference, of which the 26 full papers, 18 short papers and 6 abstracts included here were selected after peer review. The papers cover a wide range of topics in Artificial Intelligence, including machine learning, deep learning, social media evaluation, consensus-building, data science, recommender systems, and decision support systems, together with crucial applications of AI in fields such as health, education, disaster response, and the ethical impact of AI on society. The book also includes abstracts of the keynotes delivered by Professor Aida Kamišalić and Dr. Lluis Formiga. Providing a useful overview of some of the latest developments in artificial intelligence, the book will be of interest to all those working in the field.
Mastering Neural Networks
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Author : Cybellium
language : en
Publisher: Cybellium Ltd
Release Date :
Mastering Neural Networks written by Cybellium and has been published by Cybellium Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Unleash the Power of Deep Learning for Intelligent Systems In the realm of artificial intelligence and machine learning, neural networks stand as the driving force behind intelligent systems that mimic human cognition. "Mastering Neural Networks" is your ultimate guide to comprehending and harnessing the potential of these powerful algorithms, empowering you to create intelligent solutions that push the boundaries of innovation. About the Book: As technology advances, the capabilities of neural networks become more integral to various fields. "Mastering Neural Networks" offers an in-depth exploration of this cutting-edge subject—an essential toolkit for data scientists, engineers, and enthusiasts. This book caters to both newcomers and experienced learners aiming to excel in neural network concepts, architectures, and applications. Key Features: Neural Network Fundamentals: Begin by understanding the core principles of neural networks. Learn about artificial neurons, activation functions, and the architecture of these powerful algorithms. Feedforward Neural Networks: Dive into feedforward neural networks. Explore techniques for designing, training, and optimizing networks for various tasks. Convolutional Neural Networks: Grasp the art of convolutional neural networks. Understand how these architectures excel in image and pattern recognition tasks. Recurrent Neural Networks: Explore recurrent neural networks. Learn how to process sequences and time-series data, making them suitable for tasks like language modeling and speech recognition. Generative Adversarial Networks: Understand the significance of generative adversarial networks. Explore how these networks enable the generation of realistic images, text, and data. Transfer Learning and Fine-Tuning: Delve into transfer learning. Learn how to leverage pretrained models and adapt them to new tasks, saving time and resources. Neural Network Optimization: Grasp optimization techniques. Explore methods for improving network performance, reducing overfitting, and tuning hyperparameters. Real-World Applications: Gain insights into how neural networks are applied across industries. From healthcare to finance, discover the diverse applications of these algorithms. Why This Book Matters: In a world driven by intelligent systems, mastering neural networks offers a competitive advantage. "Mastering Neural Networks" empowers data scientists, engineers, and technology enthusiasts to leverage these cutting-edge algorithms, enabling them to create intelligent solutions that redefine the boundaries of innovation. Unleash the Future of Intelligence: In the landscape of artificial intelligence, neural networks are reshaping technology and innovation. "Mastering Neural Networks" equips you with the knowledge needed to leverage these powerful algorithms, enabling you to create intelligent solutions that push the boundaries of innovation and redefine what's possible. Whether you're a seasoned practitioner or new to the world of neural networks, this book will guide you in building a solid foundation for effective AI-driven solutions. Your journey to mastering neural networks starts here. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com
Navigating Challenges Of Object Detection Through Cognitive Computing
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Author : Ahmad, Sadique
language : en
Publisher: IGI Global
Release Date : 2025-04-30
Navigating Challenges Of Object Detection Through Cognitive Computing written by Ahmad, Sadique and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-30 with Computers categories.
Cognitive computing is transforming how machines perceive, analyze, and make intelligent decisions, enabling breakthroughs in object detection, segmentation, and image processing. By integrating cognitive algorithms with machine learning, this technology enhances automation, accuracy, and efficiency across industries such as healthcare, finance, and agriculture. The ability of machines to mimic human reasoning opens new frontiers for innovation, leading to smarter diagnostics, risk assessments, and precision-driven solutions. As cognitive computing evolves, its applications will continue to reshape industries, improve decision-making, and drive technological advancements that impact society on a global scale. Navigating Challenges of Object Detection Through Cognitive Computing explores the challenges of object detection across various domains and presents cognitive computing-based intelligent techniques to overcome them. It provides insights into innovative methodologies for improving detection accuracy in complex scenarios such as surface defect detection, indoor environments, adverse weather conditions, UAV imagery, and camouflaged object detection. Covering topics such as smart engineering, social medial sentiment analyses, and healthcare, this book is an excellent resource for computer engineers, computer scientists, industry practitioners, professionals, researchers, scholars, academicians, and more.
Mastering Azure Machine Learning
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Author : Christoph Körner
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-04-30
Mastering Azure Machine Learning written by Christoph Körner 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 2020-04-30 with Computers categories.
Master expert techniques for building automated and highly scalable end-to-end machine learning models and pipelines in Azure using TensorFlow, Spark, and Kubernetes Key FeaturesMake sense of data on the cloud by implementing advanced analyticsTrain and optimize advanced deep learning models efficiently on Spark using Azure DatabricksDeploy machine learning models for batch and real-time scoring with Azure Kubernetes Service (AKS)Book Description The increase being seen in data volume today requires distributed systems, powerful algorithms, and scalable cloud infrastructure to compute insights and train and deploy machine learning (ML) models. This book will help you improve your knowledge of building ML models using Azure and end-to-end ML pipelines on the cloud. The book starts with an overview of an end-to-end ML project and a guide on how to choose the right Azure service for different ML tasks. It then focuses on Azure Machine Learning and takes you through the process of data experimentation, data preparation, and feature engineering using Azure Machine Learning and Python. You'll learn advanced feature extraction techniques using natural language processing (NLP), classical ML techniques, and the secrets of both a great recommendation engine and a performant computer vision model using deep learning methods. You'll also explore how to train, optimize, and tune models using Azure Automated Machine Learning and HyperDrive, and perform distributed training on Azure. Then, you'll learn different deployment and monitoring techniques using Azure Kubernetes Services with Azure Machine Learning, along with the basics of MLOps—DevOps for ML to automate your ML process as CI/CD pipeline. By the end of this book, you'll have mastered Azure Machine Learning and be able to confidently design, build and operate scalable ML pipelines in Azure. What you will learnSetup your Azure Machine Learning workspace for data experimentation and visualizationPerform ETL, data preparation, and feature extraction using Azure best practicesImplement advanced feature extraction using NLP and word embeddingsTrain gradient boosted tree-ensembles, recommendation engines and deep neural networks on Azure Machine LearningUse hyperparameter tuning and Azure Automated Machine Learning to optimize your ML modelsEmploy distributed ML on GPU clusters using Horovod in Azure Machine LearningDeploy, operate and manage your ML models at scaleAutomated your end-to-end ML process as CI/CD pipelines for MLOpsWho this book is for This machine learning book is for data professionals, data analysts, data engineers, data scientists, or machine learning developers who want to master scalable cloud-based machine learning architectures in Azure. This book will help you use advanced Azure services to build intelligent machine learning applications. A basic understanding of Python and working knowledge of machine learning are mandatory.