Training Data For Machine Learning Models
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Training Data for Machine Learning Models
Your training data has as much to do with the success of your data project as the algorithms themselves--most failures in deep learning systems relate to training data. But while training data is the foundation for successful machine learning, there are few comprehensive resources to help you ace the process. This hands-on guide explains how to work with and scale training data. You'll gain a solid understanding of the concepts, tools, and processes needed to: Design, deploy, and ship training data for production-grade deep learning applications Integrate with a growing ecosystem of tools Recognize and correct new training data-based failure modes Improve existing system performance and avoid development risks Confidently use automation and acceleration approaches to more effectively create training data Avoid data loss by structuring metadata around created datasets Clearly explain training data concepts to subject matter experts and other shareholders Successfully maintain, operate, and improve your system.
Introduction to Machine Learning Algorithms
Author: Dr.R.Shobana
language: en
Publisher: Leilani Katie Publication
Release Date: 2024-08-23
Dr.R.Shobana, Assistant Professor, Department of Computer Applications, Sri Ramakrishna College of Arts and Science, Coimbatore, Tamil Nadu, India. Mr.T.Pradeep, Assistant Professor, Department of Computer Applications, Sri Ramakrishna College of Arts and Science, Coimbatore, Tamil Nadu, India. Mr.S.S.Saravana Kumar, Assistant Professor, Department of Computer Applications, Sri Ramakrishna College of Arts and Science, Coimbatore, Tamil Nadu, India. Dr.C.Daniel Nesa Kumar, Assistant Professor, Department of Computer Applications, Sri Ramakrishna College of Arts and Science, Coimbatore, Tamil Nadu, India. Dr.D.Arul Pon Daniel, Assistant Professor, Department of Computer Science and Applications, Loyola College of Arts and Science, Namakkal, Tamil Nadu, India.
Machine Learning and Artificial Intelligence: Concepts, Algorithms and Models
Author: Reza Rawassizadeh
language: en
Publisher: Reza Rawassizadeh
Release Date: 2025-03-15
Mastering AI, machine learning, and data science often means piecing together concepts scattered across countless resources—from statistics and visualizations to foundational models and large language models. This book, the result of eight years of effort, brings it all together in one accessible, engaging package. It clarifies artificial intelligence and data science, blending core mathematical principles with a clear, reader-friendly approach. Unlike traditional textbooks that lean heavily on equations and mathematical formalization, the author starts with minimal prerequisites, layering deeper math as the reader progresses. Each concept, algorithm, or model is unpacked through clear, hands-on examples that build the reader's skills step by step. It strikes a balance between theoretical foundations and practical application, serving as both an academic reference and a practical guide. Furthermore, the book uses humor, casual language, and comics to make the challenging concepts and topics relatable and fun. Any resemblance between the jokes and real life is pure coincidence, and no offense is intended.