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Free Download Federated Learning Theory and Practical Published 10/2024 Created by Amir Anees MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 41 Lectures ( 4h 23m ) | Size: 1.67 GB An Introduction to Federated Learning: Concepts, Implementation, and Privacy Considerations What you'll learn Learn the fundamentals and architecture of federated learning Differentiate between various types of federated learning approaches Apply federated learning in practical scenarios and combined frameworks Understand the privacy, security, and communication aspects of federated learning Requirements Basic understanding of machine learning concepts and algorithms. Familiarity with Python programming and popular ML libraries (e.g., TensorFlow, PyTorch). No prior knowledge of federated learning is required-this course will cover the essentials. Description "Federated Learning: Theory and Practical" is designed to provide you with a comprehensive introduction to one of the most exciting and evolving areas in machine learning-federated learning (FL). In an era where data privacy is becoming increasingly important, FL offers a solution by enabling machine learning models to be trained across decentralized data sources, such as smartphones or local clients, without the need to share sensitive data.This course starts with the basics of machine learning to ensure a solid foundation. You will then dive into the core concepts of federated learning, including the motivations behind its development, the different types (horizontal, vertical, and combined FL), and how it compares to traditional machine learning approaches.By week three, you'll not only grasp the theory but also be ready to implement FL systems from scratch and using popular frameworks like FLOWER. You'll explore advanced topics such as privacy-enhancing technologies, including differential privacy and homomorphic encryption, and gain insight into practical challenges like client selection and gradient inversion attacks.Whether you are a data scientist, machine learning engineer, or someone curious about privacy-preserving AI, this course offers the theoretical grounding and hands-on skills necessary to navigate the emerging landscape of federated learning. Who this course is for This course is designed for data scientists, machine learning engineers, and AI enthusiasts who want to deepen their understanding of federated learning. It's also ideal for professionals looking to apply privacy-preserving machine learning techniques in distributed environments. Whether you're familiar with machine learning or new to federated learning, this course offers valuable insights for those interested in practical implementation of FL models. Homepage https://www.udemy.com/course/federated-learning-theory-and-practical/ Screenshot Rapidgator https://rg.to/file/2b784a5dee58ce4b79ccc3aeebdb99ea/isvyi.Federated.Learning.Theory.and.Practical.part2.rar.html https://rg.to/file/65fed4c6f802c0e872c9b6eb29d6b189/isvyi.Federated.Learning.Theory.and.Practical.part1.rar.html Fikper Free Download https://fikper.com/2bMPYl5lfo/isvyi.Federated.Learning.Theory.and.Practical.part2.rar.html https://fikper.com/xVhR4Ls4jF/isvyi.Federated.Learning.Theory.and.Practical.part1.rar.html No Password - Links are Interchangeable

Free Download Federated Learning and Privacy-preserving RAGs Published 9/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 48 KHz Language: English | Size: 31.15 MB | Duration: 16m 1s Learn federated learning and privacy-preserving techniques. This course will teach you how to architect AI solutions while ensuring data privacy in Retrieval-Augmented Generation (RAG) systems. More and more organizations would like to implement Retrieval-Augmented Generation (RAG) solutions to enhance their customer experience integrating privacy-preserving techniques ensuring data security and regulatory compliance. In this course, Federated Learning and Privacy-preserving RAGs, you'll learn to design and implement advanced AI systems that prioritize data privacy without sacrificing performance. First, you'll explore the fundamentals of federated learning, including its principles and how it enables decentralized data processing. Next, you'll discover how to integrate privacy-preserving techniques into RAG models, such as homomorphic encryption and differential privacy, to safeguard sensitive information. Finally, you'll learn to implement these concepts practically, developing and deploying RAG systems that adhere to privacy regulations and protect user data. When you're finished with this course, you'll have the skills and knowledge needed to create robust, privacy-conscious RAG solutions that enhance AI performance while maintaining strict data protection standards. Homepage https://www.pluralsight.com/courses/federated-learning-privacy-preserving-rags TakeFile https://takefile.link/p81zpar7u6q9/hdtvz.Federated.Learning.and.Privacypreserving.RAGs.rar.html Rapidgator https://rg.to/file/9b0d210828f6091f9bee4737475d1de2/hdtvz.Federated.Learning.and.Privacypreserving.RAGs.rar.html Fikper Free Download https://fikper.com/XcE17yy6Gr/hdtvz.Federated.Learning.and.Privacypreserving.RAGs.rar.html No Password - Links are Interchangeable

pdf | 16.28 MB | English | Isbn:9781040126141 | Author: Amandeep Kaur (Editor), Chetna Kaushal (Editor), Md. Mehedi Hassan (Editor), Si Thu Aung (Editor) | Year: 2024 About ebook: Federated Deep Learning for Healthcare; A Practical Guide with Challenges and Opportunities https://rapidgator.net/file/8f2a686d22b837051f6f5e95fdf53788/ https://nitroflare.com/view/35E7D2085EB0F6E/