Deep Learning Dynamics The Science Behind Ai Training
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Deep Learning Dynamics The Science Behind Ai Training
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Author : ABHIJEET SARKAR
language : en
Publisher: ABHIJEET SARKAR
Release Date : 2025-01-07
Deep Learning Dynamics The Science Behind Ai Training written by ABHIJEET SARKAR and has been published by ABHIJEET SARKAR this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-07 with Computers categories.
"Deep Learning Dynamics: The Science Behind AI Training" , Written by Abhijeet Sarkar is your ultimate guide to understanding the science and art of training artificial intelligence systems. Perfect for AI enthusiasts, data scientists, researchers, and tech professionals, this comprehensive book uncovers the intricate mechanisms behind modern deep learning, providing practical insights and advanced knowledge to help you navigate the rapidly evolving AI landscape. With 34 in-depth chapters, Abhijeet Sarkar takes readers on a transformative journey through the key principles and cutting-edge techniques that define AI training. From neural network architectures and optimization strategies to handling imbalanced datasets and mitigating overfitting, this book addresses every major facet of deep learning. It's an indispensable resource for anyone looking to master the dynamic processes that power AI. What You'll Learn: Neural Network Foundations: Dive into the architecture, activation functions, and learning paradigms that form the backbone of AI. Optimization Techniques: Explore the intricacies of gradient descent, Adam, RMSProp, and other algorithms critical for efficient learning. Loss Functions and Regularization: Understand how to optimize models for accuracy while avoiding overfitting. Transfer Learning and Pretrained Models: Discover how to accelerate training and improve performance with cutting-edge methodologies. Emerging Trends: Gain insights into advanced topics like attention mechanisms, transformers, multimodal models, quantum machine learning, and the energy efficiency of AI training. Debugging and Tuning: Learn how to diagnose and enhance model performance through iterative improvements. Each chapter blends foundational theory with real-world applications, providing readers with actionable knowledge that bridges the gap between research and practice. Sarkar also delves into ethical considerations, model interpretability, and the societal implications of AI, ensuring a holistic understanding of this transformative technology. Who This Book Is For: Whether you're a seasoned professional seeking advanced insights or a beginner aiming to build a strong foundation in AI, Deep Learning Dynamics is tailored to your needs. Students, academics, and industry practitioners will all find immense value in this detailed and practical guide. Why Choose This Book? Comprehensive Coverage: Every aspect of deep learning training is explored in depth. Future-Focused Insights: Stay ahead with discussions on the latest advancements and trends. Practical Examples: Case studies and step-by-step breakdowns bring concepts to life. Expert Authorship: Written by Abhijeet Sarkar, a renowned thought leader in generative AI and digital creativity. Deep Learning Dynamics: The Science Behind AI Training isn't just a book—it's your companion in mastering the forces shaping the future of artificial intelligence. Whether you're building the next breakthrough application or exploring the potential of self-learning systems, this book equips you with the tools to succeed. Transform your understanding of AI with Deep Learning Dynamics. Order your copy today and lead the charge in the AI revolution!
The Principles Of Deep Learning Theory
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Author : Daniel A. Roberts
language : en
Publisher: Cambridge University Press
Release Date : 2022-05-26
The Principles Of Deep Learning Theory written by Daniel A. Roberts and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-26 with Computers categories.
This volume develops an effective theory approach to understanding deep neural networks of practical relevance.
Dive Into Deep Learning
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Author : Joanne Quinn
language : en
Publisher: Corwin Press
Release Date : 2019-07-15
Dive Into Deep Learning written by Joanne Quinn and has been published by Corwin Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-15 with Education categories.
The leading experts in system change and learning, with their school-based partners around the world, have created this essential companion to their runaway best-seller, Deep Learning: Engage the World Change the World. This hands-on guide provides a roadmap for building capacity in teachers, schools, districts, and systems to design deep learning, measure progress, and assess conditions needed to activate and sustain innovation. Dive Into Deep Learning: Tools for Engagement is rich with resources educators need to construct and drive meaningful deep learning experiences in order to develop the kind of mindset and know-how that is crucial to becoming a problem-solving change agent in our global society. Designed in full color, this easy-to-use guide is loaded with tools, tips, protocols, and real-world examples. It includes: • A framework for deep learning that provides a pathway to develop the six global competencies needed to flourish in a complex world — character, citizenship, collaboration, communication, creativity, and critical thinking. • Learning progressions to help educators analyze student work and measure progress. • Learning design rubrics, templates and examples for incorporating the four elements of learning design: learning partnerships, pedagogical practices, learning environments, and leveraging digital. • Conditions rubrics, teacher self-assessment tools, and planning guides to help educators build, mobilize, and sustain deep learning in schools and districts. Learn about, improve, and expand your world of learning. Put the joy back into learning for students and adults alike. Dive into deep learning to create learning experiences that give purpose, unleash student potential, and transform not only learning, but life itself.
Learning To Learn
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Author : Sebastian Thrun
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Learning To Learn written by Sebastian Thrun and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
Over the past three decades or so, research on machine learning and data mining has led to a wide variety of algorithms that learn general functions from experience. As machine learning is maturing, it has begun to make the successful transition from academic research to various practical applications. Generic techniques such as decision trees and artificial neural networks, for example, are now being used in various commercial and industrial applications. Learning to Learn is an exciting new research direction within machine learning. Similar to traditional machine-learning algorithms, the methods described in Learning to Learn induce general functions from experience. However, the book investigates algorithms that can change the way they generalize, i.e., practice the task of learning itself, and improve on it. To illustrate the utility of learning to learn, it is worthwhile comparing machine learning with human learning. Humans encounter a continual stream of learning tasks. They do not just learn concepts or motor skills, they also learn bias, i.e., they learn how to generalize. As a result, humans are often able to generalize correctly from extremely few examples - often just a single example suffices to teach us a new thing. A deeper understanding of computer programs that improve their ability to learn can have a large practical impact on the field of machine learning and beyond. In recent years, the field has made significant progress towards a theory of learning to learn along with practical new algorithms, some of which led to impressive results in real-world applications. Learning to Learn provides a survey of some of the most exciting new research approaches, written by leading researchers in the field. Its objective is to investigate the utility and feasibility of computer programs that can learn how to learn, both from a practical and a theoretical point of view.
Harnessing Machine Learning To Decode Plant Microbiome Dynamics For Sustainable Agriculture
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Author : Eman Mohammad Khalaf
language : en
Publisher: Frontiers Media SA
Release Date : 2025-06-17
Harnessing Machine Learning To Decode Plant Microbiome Dynamics For Sustainable Agriculture written by Eman Mohammad Khalaf 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 2025-06-17 with Science categories.
The field of plant-associated microbiomes has garnered significant attention due to its potential to address global food insecurity, particularly in low and middle-income countries. As the global population continues to grow, improving crop yield and productivity through advanced breeding programs has become imperative. Plants and their associated microbial communities have co-evolved over millennia, forming intricate relationships that significantly impact plant health and fitness. Recent studies have highlighted the benefits of these microbial communities, such as enhanced growth, improved nutrient uptake, and increased tolerance to environmental stresses. Despite these advancements, traditional methods of analyzing multi-omics data—such as meta-genomics, meta-transcriptomics, and meta-proteomics—are often inadequate. These methods struggle with false positives and fail to capture the interaction effects between variables, leaving gaps in our understanding of how microbiomes influence plant phenotypes. The advent of machine-learning (ML) algorithms has revolutionized this field, offering new ways to analyze complex microbiome data and predict their impact on plant traits.
Artificial Intelligence Machine Learning And Blockchain In Quantum Satellite Drone And Network
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Author : Thiruselvan Subramanian
language : en
Publisher: CRC Press
Release Date : 2022-10-14
Artificial Intelligence Machine Learning And Blockchain In Quantum Satellite Drone And Network written by Thiruselvan Subramanian and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-14 with Technology & Engineering categories.
Quantum computing is a field in which advanced technologies like quantum communication, artificial intelligence and machine learning can be used to secure and speed up connectivity using quantum computers, quantum drones or quantum satellites. This book serve as a foundation for researchers and scientists in this field. Future technologies, such as quantum drone delivery systems, quicker internet and climate change mitigation, will need quantum information processing and quantum computation. This book deeply explores the importance of quantum computing in real-time applications. It may be used as a reference book for students in higher education, including undergraduate and graduate students, as well as researchers. Key features: Provides a clear insight into the Internet of Drones for academicians, postdoc fellows, research scholars, graduate and postgraduate students, industry fellows and software engineers Useful to professionals who seek information about the Internet of Drones, including experts in quantum computing and physics and post-quantum cryptography, as well as data scientists and data analysts Covers quantum computing and security for Unmanned Aerial Vehicles (UAV) or drones which are widely useful for applications such as military, government, and non-government systems Explores futuristic aspects of the Intenet of Drones to improve everyday living for ordinary people
Data Driven Science And Engineering
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Author : Steven L. Brunton
language : en
Publisher: Cambridge University Press
Release Date : 2022-05-05
Data Driven Science And Engineering written by Steven L. Brunton and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-05 with Computers categories.
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Driving Scientific And Engineering Discoveries Through The Integration Of Experiment Big Data And Modeling And Simulation
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Author : Jeffrey Nichols
language : en
Publisher: Springer Nature
Release Date : 2022-03-09
Driving Scientific And Engineering Discoveries Through The Integration Of Experiment Big Data And Modeling And Simulation written by Jeffrey Nichols and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-09 with Computers categories.
This book constitutes the revised selected papers of the 21st Smoky Mountains Computational Sciences and Engineering Conference, SMC 2021, held in Oak Ridge, TN, USA*, in October 2021. The 33 full papers and 3 short papers presented were carefully reviewed and selected from a total of 88 submissions. The papers are organized in topical sections of computational applications: converged HPC and artificial intelligence; advanced computing applications: use cases that combine multiple aspects of data and modeling; advanced computing systems and software: connecting instruments from edge to supercomputers; deploying advanced computing platforms: on the road to a converged ecosystem; scientific data challenges. *The conference was held virtually due to the COVID-19 pandemic.
Electronic Government
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Author : Ida Lindgren
language : en
Publisher: Springer Nature
Release Date : 2023-08-19
Electronic Government written by Ida Lindgren and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-19 with Computers categories.
This book constitutes the proceedings of the 22nd IFIP WG 8.5 International Conference on Electronic Government, EGOV 2023, which took place in Budapest, Hungary, during September 5–7, 2023.The 28 full papers included in this book were carefully reviewed and selected from a total of 106 submissions. They were organized in topical sections as follows: Digital government; artificial intelligence, algorithms, and automation; open government and open data; smart cities, regions, and societies; innovation and transformation in government.
Deterministic Artificial Intelligence
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Author : Timothy Sands
language : en
Publisher: BoD – Books on Demand
Release Date : 2020-05-27
Deterministic Artificial Intelligence written by Timothy Sands and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-27 with Computers categories.
Kirchhoff’s laws give a mathematical description of electromechanics. Similarly, translational motion mechanics obey Newton’s laws, while rotational motion mechanics comply with Euler’s moment equations, a set of three nonlinear, coupled differential equations. Nonlinearities complicate the mathematical treatment of the seemingly simple action of rotating, and these complications lead to a robust lineage of research culminating here with a text on the ability to make rigid bodies in rotation become self-aware, and even learn. This book is meant for basic scientifically inclined readers commencing with a first chapter on the basics of stochastic artificial intelligence to bridge readers to very advanced topics of deterministic artificial intelligence, espoused in the book with applications to both electromechanics (e.g. the forced van der Pol equation) and also motion mechanics (i.e. Euler’s moment equations). The reader will learn how to bestow self-awareness and express optimal learning methods for the self-aware object (e.g. robot) that require no tuning and no interaction with humans for autonomous operation. The topics learned from reading this text will prepare students and faculty to investigate interesting problems of mechanics. It is the fondest hope of the editor and authors that readers enjoy the book.