Machine Learning Of Design Concepts
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Machine Learning Of Design Concepts
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Author : Heng Li
language : en
Publisher: Computational Mechanics
Release Date : 1994
Machine Learning Of Design Concepts written by Heng Li and has been published by Computational Mechanics this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Computers categories.
Reviews the findings and trends of recent research on machine learning techniques and their applications in engineering design. Also presents a machine learning system that automatically generates design concepts from previous design examples. Includes abstracts of seven research papers. No index. Annotation copyright by Book News, Inc., Portland, OR
Collaborated Machine Learning Based Design Soluion To Conceptual Design Of Architecture
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Author : Ruixin Wang
language : en
Publisher:
Release Date : 2022
Collaborated Machine Learning Based Design Soluion To Conceptual Design Of Architecture written by Ruixin Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Although artificial intelligence technology with its core mechanism, machine learning or deep learning, becomes an influential and trending topic in the field of architecture design, there exist obstacles to the application of automated AI design to the professional architecture practice. The thesis analysis the essential elements required for the machine learning algorithm to accomplish the task of conceptual design involves the capabilities of knowledge, perception, and creativity that are seemingly only possessed by human designers. However, machine learning agents associated with Convolution Neural Networks, Deep-Q Learning, and Generative Adversarial Networks can be proved to achieve the capabilities mentioned above by their mechanism. Although there exists certain constraints, setbacks, and bias, machine learning agent, particularly VQGAN + CLIP, has revealed notable potential in architectural conceptual design where its aesthetic creativity and spatial perception can match with professional human architects because of its remarkable mechanism relating visual objects to abstract texts backed by computing power and big-data era.
Deep Learning Concepts And Architectures
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Author : Witold Pedrycz
language : en
Publisher: Springer Nature
Release Date : 2019-10-29
Deep Learning Concepts And Architectures written by Witold Pedrycz and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-29 with Technology & Engineering categories.
This book introduces readers to the fundamental concepts of deep learning and offers practical insights into how this learning paradigm supports automatic mechanisms of structural knowledge representation. It discusses a number of multilayer architectures giving rise to tangible and functionally meaningful pieces of knowledge, and shows how the structural developments have become essential to the successful delivery of competitive practical solutions to real-world problems. The book also demonstrates how the architectural developments, which arise in the setting of deep learning, support detailed learning and refinements to the system design. Featuring detailed descriptions of the current trends in the design and analysis of deep learning topologies, the book offers practical guidelines and presents competitive solutions to various areas of language modeling, graph representation, and forecasting.
Machine Learning For Human Design
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Author : Bryan Wen Xi Ong
language : en
Publisher:
Release Date : 2021
Machine Learning For Human Design written by Bryan Wen Xi Ong and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Formal computational approaches in the realm of engineering and architecture, such as parametric modelling and optimization, are becoming increasingly powerful, allowing for systematic and rigorous design processes. However, these methods often bring a steep learning curve, require previous expertise, or are unintuitive and unnatural to human design. On the other hand, analog design methods such as hand sketching are commonly used by architects and engineers alike. They constitute quick, easy, and almost primal modes of generating and transferring design concepts, which in turn facilitates the sharing of ideas and feedback. In the advent of increasing computational power and developments in data analysis, deep learning, and other emerging technologies, there is a potential to bridge the gap between these seemingly divergent processes to develop new hybrid approaches to design. Such methods can provide designers with new opportunities to harness the systematic and data-driven power of computation and performance analysis while maintaining a more creative and intuitive design interface. This thesis presents a new method for interpreting human designs in sketch format and predicting their structural performance using recent advances in deep learning. Furthermore, the thesis will also demonstrate how this new technique can be used in design workflows including performance-based guidance and interpolations between concepts.
Embedded Deep Learning
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Author : Bert Moons
language : en
Publisher: Springer
Release Date : 2018-10-23
Embedded Deep Learning written by Bert Moons and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-23 with Technology & Engineering categories.
This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic design approaches on the application-, algorithmic-, computer architecture-, and circuit-level that will help in achieving the goal of reducing the computational cost of deep learning algorithms. The impact of these techniques is displayed in four silicon prototypes for embedded deep learning. Gives a wide overview of a series of effective solutions for energy-efficient neural networks on battery constrained wearable devices; Discusses the optimization of neural networks for embedded deployment on all levels of the design hierarchy – applications, algorithms, hardware architectures, and circuits – supported by real silicon prototypes; Elaborates on how to design efficient Convolutional Neural Network processors, exploiting parallelism and data-reuse, sparse operations, and low-precision computations; Supports the introduced theory and design concepts by four real silicon prototypes. The physical realization’s implementation and achieved performances are discussed elaborately to illustrated and highlight the introduced cross-layer design concepts.
Designing Machine Learning Systems With Python
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Author : David Julian
language : en
Publisher:
Release Date : 2016-04-04
Designing Machine Learning Systems With Python written by David Julian and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-04 with Computers categories.
Design efficient machine learning systems that give you more accurate resultsAbout This Book- Gain an understanding of the machine learning design process- Optimize machine learning systems for improved accuracy- Understand common programming tools and techniques for machine learning- Develop techniques and strategies for dealing with large amounts of data from a variety of sources- Build models to solve unique tasksWho This Book Is ForThis book is for data scientists, scientists, or just the curious. To get the most out of this book, you will need to know some linear algebra and some Python, and have a basic knowledge of machine learning concepts.What You Will Learn- Gain an understanding of the machine learning design process- Optimize the error function of your machine learning system- Understand the common programming patterns used in machine learning- Discover optimizing techniques that will help you get the most from your data- Find out how to design models uniquely suited to your taskIn DetailMachine learning is one of the fastest growing trends in modern computing. It has applications in a wide range of fields, including economics, the natural sciences, web development, and business modeling. In order to harness the power of these systems, it is essential that the practitioner develops a solid understanding of the underlying design principles.There are many reasons why machine learning models may not give accurate results. By looking at these systems from a design perspective, we gain a deeper understanding of the underlying algorithms and the optimisational methods that are available. This book will give you a solid foundation in the machine learning design process, and enable you to build customised machine learning models to solve unique problems. You may already know about, or have worked with, some of the off-the-shelf machine learning models for solving common problems such as spam detection or movie classification, but to begin solving more complex problems, it is important to adapt these models to your own specific needs. This book will give you this understanding and more.Style and approachThis easy-to-follow, step-by-step guide covers the most important machine learning models and techniques from a design perspective.
Design Computing And Cognition 22
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Author : John S Gero
language : en
Publisher: Springer Nature
Release Date : 2023-01-04
Design Computing And Cognition 22 written by John S Gero and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-04 with Technology & Engineering categories.
This book reports research and development that represent the state of the art in artificial intelligence in design, design cognition, design neurocognition, and design theories from the Tenth International Conference on Design Computing and Cognition held in Glasgow, UK, in 2022. The 48 chapters are grouped under the headings of natural language processing and design; design cognition; design neurocognition; learning and design; creative design and co-design; shape grammars; quantum computing; and human behavior. These contributions are of particular interest to design researchers and design educators, as well as to users of advanced computation and cognitive science. This book contains knowledge about the cognitive and neurocognitive behavior of designers, which is valuable to those who need to gain a better understanding of designing.
Design Patterns Of Deep Learning With Tensorflow
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Author : Thomas V Joseph
language : en
Publisher: BPB Publications
Release Date : 2024-06-06
Design Patterns Of Deep Learning With Tensorflow written by Thomas V Joseph and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-06 with Computers categories.
Architecting AI: Design patterns for building deep learning products KEY FEATURES ● Master foundational concepts in design patterns of deep learning. ● Benefit from practical insights shared by an industry professional. ● Learn to build data products using deep learning. DESCRIPTION Design Patterns of Deep Learning with TensorFlow is your comprehensive guide to learning deep learning from a design pattern perspective. In this book, we explore deep learning within the context of building hyper-personalization models, exploring its applications across various industries and scenarios. It starts by showing how deep learning enhances retail through customer segmentation and data analysis. You will learn neural networks, computer vision with CNNs, and NLP for analyzing customer behavior. This book addresses challenges like uneven data and optimizing models with techniques like backpropagation, hyperparameter tuning, and transfer learning. Finally, it covers setting up data pipelines and deploying your system. With practical tips and actionable advice, this book equips readers with the skills and strategies needed to thrive in today's competitive AI landscape. By the end of this book, you will be equipped with the knowledge and practical skills to build and deploy deep learning-powered hyper-personalization systems that deliver exceptional customer experiences. WHAT YOU WILL LEARN ● Understand about hyper-personalized AI models for tailored user experiences. ● Design principles of computer vision and NLP models. ● Inner working of transformers equipping readers to understand the intricacies of generative AI and large language models (LLMs) like ChatGPT. ● To get the best out of deep learning models through hyperparameter tuning and transfer learning. ● Learn how to build deployment pipelines to serve models into production environments seamlessly. WHO THIS BOOK IS FOR This book caters to both beginners and experienced practitioners in the field of data science and Machine Learning. Through practical examples, it simplifies complex ideas, linking them to design patterns. TABLE OF CONTENTS 1. Customer Hyper-personalization 2. Introduction to Design Patterns and Neural Networks 3. Design Patterns in Visual Representation Learning 4. Design Patterns for Non-Visual Representation Learning 5. Design Patterns for Transformers 6. Data Distribution Challenges and Strategies 7. Model Training Philosophies 8. Hyperparameter Tuning 9. Transfer Learning 10. Setting Up Data and Deployment Pipelines
Machine Learning Design Patterns
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Author : Valliappa Lakshmanan
language : en
Publisher: O'Reilly Media
Release Date : 2020-10-15
Machine Learning Design Patterns written by Valliappa Lakshmanan and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-15 with Computers categories.
The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly
Deep Learning Networks
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Author : Jayakumar Singaram
language : en
Publisher: Springer Nature
Release Date : 2023-12-03
Deep Learning Networks written by Jayakumar Singaram and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-03 with Technology & Engineering categories.
This textbook presents multiple facets of design, development and deployment of deep learning networks for both students and industry practitioners. It introduces a deep learning tool set with deep learning concepts interwoven to enhance understanding. It also presents the design and technical aspects of programming along with a practical way to understand the relationships between programming and technology for a variety of applications. It offers a tutorial for the reader to learn wide-ranging conceptual modeling and programming tools that animate deep learning applications. The book is especially directed to students taking senior level undergraduate courses and to industry practitioners interested in learning about and applying deep learning methods to practical real-world problems.