Scalable Deep Learning In Practice
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Scalable Deep Learning In Practice
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Author : Dr Adrian Devlin
language : en
Publisher: Independently Published
Release Date : 2025-11-14
Scalable Deep Learning In Practice written by Dr Adrian Devlin and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-14 with Computers categories.
Unlock the Power of Deep Learning-No Experience Needed! Are you fascinated by artificial intelligence, but intimidated by complex code, jargon, or the fear of making mistakes? Do you wish for a patient guide to walk you step-by-step through modern machine learning-without assuming you're already an expert? You're not alone, and you're in the right place. Scalable Deep Learning in Practice is your welcoming companion on the journey from beginner to confident deep learning practitioner. This hands-on book is designed for readers just like you: complete beginners, self-taught learners, and curious coders eager to turn ambition into real-world AI skills. You don't need a technical background to succeed. Every chapter breaks down intimidating concepts into clear, friendly explanations and walks you through building, training, and deploying powerful neural networks using PyTorch Lightning and ONNX. Each lesson celebrates small victories and normalizes mistakes, because learning should be encouraging, not overwhelming. Inside this book, you'll discover: Step-by-Step Guidance: Start from zero and build up-install tools, write your first Python code, and create your own deep learning models with confidence. Practical Projects: Work through real image and text classification projects, see your code in action, and develop skills you can use right away. Modern, Scalable Workflows: Learn how to train models faster, track experiments, and deploy your solutions anywhere-from your laptop to the cloud-using industry-leading tools. Mistakes Are Welcome: Enjoy a supportive learning environment where confusion is normal and every "aha!" moment is worth celebrating. Real-World Applications: Go beyond theory-discover how to take your models from idea to production with clear instructions on exporting to ONNX and serving with FastAPI. Personal Insights & Encouragement: Benefit from honest stories, troubleshooting tips, and practical advice from someone who knows what it's like to start from scratch. Whether you dream of building your own AI apps, landing a job in data science, or simply understanding what's behind today's smartest technologies, this book is your empowering first step. Key topics covered include: Deep learning for beginners PyTorch Lightning tutorial ONNX model deployment Reproducible machine learning workflows FastAPI and real-world ML API deployment Scaling projects for speed and reliability No experience? No problem! Open the first page, follow along at your own pace, and watch your confidence grow as you master the essentials of scalable deep learning. Ready to unlock your AI potential? Start your learning adventure today with a book that's as supportive as it is practical-your journey to real-world deep learning begins right here.
Mlops In Practice
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Author : Diego Rodrigues
language : en
Publisher: StudioD21
Release Date : 2025-02-11
Mlops In Practice written by Diego Rodrigues and has been published by StudioD21 this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-11 with Business & Economics categories.
MLOps IN PRACTICE is an essential guide for professionals looking to take Machine Learning models from experimentation to production with efficiency, scalability, and continuous automation. In this book, you will learn how to implement robust pipelines, monitor AI models in real time, and apply the best MLOps practices to ensure performance, reliability, and governance in Artificial Intelligence projects. Written by Diego Rodrigues, a best-selling author with over 180 titles published in six languages, this book combines theory and practice, offering a modern and applied approach to the current MLOps landscape. Throughout the chapters, you will explore essential frameworks and tools such as Docker, Kubernetes, CI/CD for Machine Learning, MLflow, TensorFlow Extended (TFX), FastAPI, and more. You will learn how to: Automate and scale Machine Learning pipelines with advanced versioning and monitoring techniques. Implement CI/CD for AI models, ensuring continuous training, deployment, and retraining. Manage models in production by applying observability, traceability, and bias mitigation practices. Utilize leading industry tools such as Kubeflow, MLflow, Airflow, and TFX to orchestrate ML workflows. Enhance AI governance and security, ensuring compliance with regulations and international standards. With practical examples, case studies, and established frameworks, MasterTech: MLOps in Practice is not just a technical manual—it is an indispensable resource for data scientists, ML engineers, software architects, and technology leaders looking to implement MLOps strategically and at scale. Get ready to revolutionize the way you manage AI models in production and master the most advanced MLOps techniques in 2025! TAGS: Python Java Linux Kali HTML ASP.NET Ada Assembly BASIC Borland Delphi C C# C++ CSS Cobol Compilers DHTML Fortran General JavaScript LISP PHP Pascal Perl Prolog RPG Ruby SQL Swift UML Elixir Haskell VBScript Visual Basic XHTML XML XSL Django Flask Ruby on Rails Angular React Vue.js Node.js Laravel Spring Hibernate .NET Core Express.js TensorFlow PyTorch Jupyter Notebook Keras Bootstrap Foundation jQuery SASS LESS Scala Groovy MATLAB R Objective-C Rust Go Kotlin TypeScript Dart SwiftUI Xamarin React Native NumPy Pandas SciPy Matplotlib Seaborn D3.js OpenCV NLTK PySpark BeautifulSoup Scikit-learn XGBoost CatBoost LightGBM FastAPI Redis RabbitMQ Kubernetes Docker Jenkins Terraform Ansible Vagrant GitHub GitLab CircleCI Regression Logistic Regression Decision Trees Random Forests AI ML K-Means Clustering Support Vector Machines Gradient Boosting Neural Networks LSTMs CNNs GANs ANDROID IOS MACOS WINDOWS Nmap Metasploit Framework Wireshark Aircrack-ng John the Ripper Burp Suite SQLmap Maltego Autopsy Volatility IDA Pro OllyDbg YARA Snort ClamAV Netcat Tcpdump Foremost Cuckoo Sandbox Fierce HTTrack Kismet Hydra Nikto OpenVAS Nessus ZAP Radare2 Binwalk GDB OWASP Amass Dnsenum Dirbuster Wpscan Responder Setoolkit Searchsploit Recon-ng BeEF AWS Google Cloud IBM Azure Databricks Nvidia Meta Power BI IoT CI/CD Hadoop Spark Dask SQLAlchemy Web Scraping MySQL Big Data Science OpenAI ChatGPT Handler RunOnUiThread() Qiskit Q# Cassandra Bigtable VIRUS MALWARE Information Pen Test Cybersecurity Linux Distributions Ethical Hacking Vulnerability Analysis System Exploration Wireless Attacks Web Application Security Malware Analysis Social Engineering Social Engineering Toolkit SET Computer Science IT Professionals Careers Expertise Library Training Operating Systems Security Testing Penetration Test Cycle Mobile Techniques Industry Global Trends Tools Framework Network Security Courses Tutorials Challenges Landscape Cloud Threats Compliance Research Technology Flutter Ionic Web Views Capacitor APIs REST GraphQL Firebase Redux Provider Bitrise Actions Material Design Cupertino Fastlane Appium Selenium Jest Visual Studio AR VR sql deepseek mysql startup digital marketing
Scalable And Distributed Machine Learning And Deep Learning Patterns
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Author : Thomas, J. Joshua
language : en
Publisher: IGI Global
Release Date : 2023-08-25
Scalable And Distributed Machine Learning And Deep Learning Patterns written by Thomas, J. Joshua and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-25 with Computers categories.
Scalable and Distributed Machine Learning and Deep Learning Patterns is a practical guide that provides insights into how distributed machine learning can speed up the training and serving of machine learning models, reduce time and costs, and address bottlenecks in the system during concurrent model training and inference. The book covers various topics related to distributed machine learning such as data parallelism, model parallelism, and hybrid parallelism. Readers will learn about cutting-edge parallel techniques for serving and training models such as parameter server and all-reduce, pipeline input, intra-layer model parallelism, and a hybrid of data and model parallelism. The book is suitable for machine learning professionals, researchers, and students who want to learn about distributed machine learning techniques and apply them to their work. This book is an essential resource for advancing knowledge and skills in artificial intelligence, deep learning, and high-performance computing. The book is suitable for computer, electronics, and electrical engineering courses focusing on artificial intelligence, parallel computing, high-performance computing, machine learning, and its applications. Whether you're a professional, researcher, or student working on machine and deep learning applications, this book provides a comprehensive guide for creating distributed machine learning, including multi-node machine learning systems, using Python development experience. By the end of the book, readers will have the knowledge and abilities necessary to construct and implement a distributed data processing pipeline for machine learning model inference and training, all while saving time and costs.
Mastering The Minds Of Machines
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Author : Laith Abualigah
language : en
Publisher: CRC Press
Release Date : 2025-09-09
Mastering The Minds Of Machines written by Laith Abualigah and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-09 with Computers categories.
The book unravels fundamental concepts that underpin deep learning, allowing even those without prior technical knowledge to grasp the intricacies of neural networks and machine learning algorithms. It provides roadmap to understanding the key principles, from the simplest perceptron to the most advanced convolutional and recurrent networks, explaining how they can perceive, learn, and make intelligent decisions. Real-world applications of deep learning and AI are given, showcasing how these technologies have transformed industries such as healthcare, finance, and self-driving cars. Case studies and expert insights provide valuable perspectives on the enormous potential and ethical challenges in the field. The book bridges the gap between theoretical concepts and practical implementation. It empowers readers to embark on their own AI journeys, with step-by-step guidance on building and training neural networks, working with popular frameworks, and handling big data. As the AI and deep learning landscape evolves rapidly, this book keeps pace. It delves into emerging trends such as generative adversarial networks (GANs), reinforcement learning, and the ethical considerations surrounding AI development. An essential reading for AI enthusiasts, students, and professionals alike. It provides the knowledge and tools to harness the potential of intelligent machines and contribute to the ongoing AI revolution.
Deep Learning And Parallel Computing Environment For Bioengineering Systems
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Author : Arun Kumar Sangaiah
language : en
Publisher: Academic Press
Release Date : 2019-07-26
Deep Learning And Parallel Computing Environment For Bioengineering Systems written by Arun Kumar Sangaiah and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-26 with Technology & Engineering categories.
Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning paradigms is the major strength of this book. This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians. The proposed techniques and concepts in this book can be extended in future to accommodate changing business organizations' needs as well as practitioners' innovative ideas. - Presents novel, in-depth research contributions from a methodological/application perspective in understanding the fusion of deep machine learning paradigms and their capabilities in solving a diverse range of problems - Illustrates the state-of-the-art and recent developments in the new theories and applications of deep learning approaches applied to parallel computing environment in bioengineering systems - Provides concepts and technologies that are successfully used in the implementation of today's intelligent data-centric critical systems and multi-media Cloud-Big data
Azure Ml Pipelines In Practice
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Author : William Smith
language : en
Publisher: HiTeX Press
Release Date : 2025-08-19
Azure Ml Pipelines In Practice written by William Smith and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-19 with Computers categories.
"Azure ML Pipelines in Practice" Azure ML Pipelines in Practice is a comprehensive guide for machine learning engineers, data scientists, and DevOps professionals seeking to master the design, deployment, and management of end-to-end ML pipelines on the Azure platform. Beginning with fundamental concepts and architecture, the book navigates through core pipeline frameworks, secure environment setup, and orchestration strategies, providing readers with the practical knowledge needed to harness the full power of Azure Machine Learning services. Each chapter is meticulously structured to build both theoretical understanding and operational competence, addressing critical topics such as security, identity management, and environment configuration. Moving beyond the basics, the book delves into the intricacies of data engineering, scalable component design, and advanced workflow orchestration. Readers will learn essential techniques for data integration, versioning, and transformation, together with robust approaches to validation and privacy compliance. The treatment of modular and reusable component development is complemented by in-depth coverage of error handling, conditioning, parallelism, and efficient resource management—empowering practitioners to create maintainable, testable, and production-grade pipelines. The later chapters focus on real-world applications, including distributed training, hyperparameter tuning, automated model evaluation, and deployment automation. The book addresses CI/CD integration, infrastructure-as-code strategies, and operational monitoring for ongoing pipeline health, while also tackling the nuances of scaling, governance, cost management, and global deployment across enterprise environments. Advanced patterns and emerging directions—such as hybrid and multi-cloud orchestration, event-driven flows, edge/IoT integration, and extensibility with open-source tools—round out the volume, making Azure ML Pipelines in Practice an indispensable resource for building resilient and future-ready ML workflows in the cloud.
Partnership And Transformation
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Author : Leda Stott
language : en
Publisher: Taylor & Francis
Release Date : 2022-11-24
Partnership And Transformation written by Leda Stott and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-24 with Business & Economics categories.
Over the last 30 years, partnership has received growing attention across a range of sectors and disciplines. Widely used to describe a relationship in which different actors pool their resources, knowledge and skills to address common problems, partnership is currently presented as central to the achievement of more inclusive and sustainable development. Rejecting "one size fits all" approaches, and mindful of different understandings of the term, Partnership and Transformation: The Promise of Multi-stakeholder Collaboration in Context, which is designed to appeal to both academics and practitioners alike, argues that partnership must be understood in relation to specific contexts and the added value it may offer for individuals, organisations and wider society. It is further suggested that the transformational potential of partnership rests critically upon a move away from purely instrumental considerations of its worth to a deeper appreciation of its intrinsic value as a process based on interpersonal relationships. A stronger balance between pragmatic and reflective dimensions of partnership can, the author claims, enhance opportunities for meaningful deliberation and productive conflict and contribute to the systems change needed for a global citizenship that embraces human well-being and stewardship of the planet.
Artificial Intelligence Techniques For Advanced Computing Applications
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Author : D. Jude Hemanth
language : en
Publisher: Springer Nature
Release Date : 2020-07-23
Artificial Intelligence Techniques For Advanced Computing Applications written by D. Jude Hemanth and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-23 with Technology & Engineering categories.
This book features a collection of high-quality research papers presented at the International Conference on Advanced Computing Technology (ICACT 2020), held at the SRM Institute of Science and Technology, Chennai, India, on 23–24 January 2020. It covers the areas of computational intelligence, artificial intelligence, machine learning, deep learning, big data, and applications of artificial intelligence in networking, IoT and bioinformatics
Rewilding In Practice
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Author : Ian Convery
language : en
Publisher: Frontiers Media SA
Release Date : 2025-03-03
Rewilding In Practice written by Ian Convery and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-03 with Science categories.
Rewilding is a long-term and large-scale process which works to restore degraded ecosystems or social-ecological systems to the point that they are resilient and self-sustaining and therefore requiring little in terms of conservation management. Rewilding practice focuses on re-establishing ecological processes, including trophic levels, that were lost, mainly due to anthropogenic disturbance. It also aims to affect a paradigm shift in human-nature relationships, with intentions to better understand the interdependencies between humans and nature so that people can appreciate and accommodate nature in their landscapes, and make more informed and sustainable decisions about how we live. In this sense, rewilding has real transformational potential.
Python Real World Machine Learning
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Author : Prateek Joshi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-11-14
Python Real World Machine Learning written by Prateek Joshi 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 2016-11-14 with Computers categories.
Learn to solve challenging data science problems by building powerful machine learning models using Python About This Book Understand which algorithms to use in a given context with the help of this exciting recipe-based guide This practical tutorial tackles real-world computing problems through a rigorous and effective approach Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale Who This Book Is For This Learning Path is for Python programmers who are looking to use machine learning algorithms to create real-world applications. It is ideal for Python professionals who want to work with large and complex datasets and Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. Experience with Python, Jupyter Notebooks, and command-line execution together with a good level of mathematical knowledge to understand the concepts is expected. Machine learning basic knowledge is also expected. What You Will Learn Use predictive modeling and apply it to real-world problems Understand how to perform market segmentation using unsupervised learning Apply your new-found skills to solve real problems, through clearly-explained code for every technique and test Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms Increase predictive accuracy with deep learning and scalable data-handling techniques Work with modern state-of-the-art large-scale machine learning techniques Learn to use Python code to implement a range of machine learning algorithms and techniques In Detail Machine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is transforming the way we understand and interact with the world around us. In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. The second module, Advanced Machine Learning with Python, is designed to take you on a guided tour of the most relevant and powerful machine learning techniques and you'll acquire a broad set of powerful skills in the area of feature selection and feature engineering. The third module in this learning path, Large Scale Machine Learning with Python, dives into scalable machine learning and the three forms of scalability. It covers the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python. This Learning Path will teach you Python machine learning for the real world. The machine learning techniques covered in this Learning Path are at the forefront of commercial practice. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Python Machine Learning Cookbook by Prateek Joshi Advanced Machine Learning with Python by John Hearty Large Scale Machine Learning with Python by Bastiaan Sjardin, Alberto Boschetti, Luca Massaron Style and approach This course is a smooth learning path that will teach you how to get started with Python machine learning for the real world, and develop solutions to real-world problems. Through this comprehensive course, you'll learn to create the most effective machine learning techniques from scratch and more!