Deep Learning From First Principles
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Deep Learning From Scratch
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Author : Seth Weidman
language : en
Publisher: O'Reilly Media
Release Date : 2019-11-04
Deep Learning From Scratch written by Seth Weidman 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 2019-11-04 with Computers categories.
With the reinvigoration of neural networks in the 2000s, deep learning is now paving the way for modern machine learning. This practical book provides a solid foundation in how deep learning works for data scientists and software engineers with a background in machine learning. Author Seth Weidman shows you how to implement multilayer neural networks, convolutional neural networks, and recurrent neural networks from scratch. Using these networks as building blocks, you'll learn how to build advanced architectures such as image captioning and Neural Turing machines (NTMs). You'll also explore the math behind the theories.
Machine Learning Accelerated First Principles Predictions Of The Stability And Mechanical Properties Of L12 Strengthened Cobalt Based Superalloys
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Author : Shengkun Xi
language : en
Publisher: OAE Publishing Inc.
Release Date : 2022-09-20
Machine Learning Accelerated First Principles Predictions Of The Stability And Mechanical Properties Of L12 Strengthened Cobalt Based Superalloys written by Shengkun Xi and has been published by OAE Publishing Inc. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-20 with Technology & Engineering categories.
As promising next-generation candidates for applications in aero-engines, L12-strengthened cobalt (Co)-based superalloys have attracted extensive attention. However, the L12 strengthening phase in first-generation Co-Al-W-based superalloys is metastable, and both its solvus temperature and mechanical properties still need improvement. Therefore, it is necessary to discover new L12-strengthened Co-based superalloy systems with a stable L12 phase by exploring the effect of alloying elements on their stability. Traditional first-principles calculations are capable of providing the crystal structure and mechanical properties of the L12 phase doped by transition metals but suffer from low efficiency and relatively high computational costs. The present study combines machine learning (ML) with first-principles calculations to accelerate crystal structure and mechanical property predictions, with the latter providing both the training and validation datasets. Three ML models are established and trained to predict the occupancy of alloying elements in the supercell and the stability and mechanical properties of the L12 phase. The ML predictions are evaluated using first-principles calculations and the accompanying data are used to further refine the ML models. Our ML-accelerated first-principles calculation approach offers more efficient predictions of the crystal structure and mechanical properties for Co-V-Ta- and Co-Al-V-based systems than the traditional counterpart. This approach is applicable to expediting crystal structure and mechanical property calculations and thus the design and discovery of other advanced materials beyond Co-based superalloys.
Learning Theory From First Principles
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Author : Francis Bach
language : en
Publisher: MIT Press
Release Date : 2024-12-24
Learning Theory From First Principles written by Francis Bach and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-24 with Computers categories.
A comprehensive and cutting-edge introduction to the foundations and modern applications of learning theory. Research has exploded in the field of machine learning resulting in complex mathematical arguments that are hard to grasp for new comers. . In this accessible textbook, Francis Bach presents the foundations and latest advances of learning theory for graduate students as well as researchers who want to acquire a basic mathematical understanding of the most widely used machine learning architectures. Taking the position that learning theory does not exist outside of algorithms that can be run in practice, this book focuses on the theoretical analysis of learning algorithms as it relates to their practical performance. Bach provides the simplest formulations that can be derived from first principles, constructing mathematically rigorous results and proofs without overwhelming students. Provides a balanced and unified treatment of most prevalent machine learning methods Emphasizes practical application and features only commonly used algorithmic frameworks Covers modern topics not found in existing texts, such as overparameterized models and structured prediction Integrates coverage of statistical theory, optimization theory, and approximation theory Focuses on adaptivity, allowing distinctions between various learning techniques Hands-on experiments, illustrative examples, and accompanying code link theoretical guarantees to practical behaviors
Data Driven Modelling And Scientific Machine Learning In Continuum Physics
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Author : Krishna Garikipati
language : en
Publisher: Springer Nature
Release Date : 2024-07-29
Data Driven Modelling And Scientific Machine Learning In Continuum Physics written by Krishna Garikipati and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-29 with Mathematics categories.
This monograph takes the reader through recent advances in data-driven methods and machine learning for problems in science—specifically in continuum physics. It develops the foundations and details a number of scientific machine learning approaches to enrich current computational models of continuum physics, or to use the data generated by these models to infer more information on these problems. The perspective presented here is drawn from recent research by the author and collaborators. Applications drawn from the physics of materials or from biophysics illustrate each topic. Some elements of the theoretical background in continuum physics that are essential to address these applications are developed first. These chapters focus on nonlinear elasticity and mass transport, with particular attention directed at descriptions of phase separation. This is followed by a brief treatment of the finite element method, since it is the most widely used approach to solve coupled partial differential equations in continuum physics. With these foundations established, the treatment proceeds to a number of recent developments in data-driven methods and scientific machine learning in the context of the continuum physics of materials and biosystems. This part of the monograph begins by addressing numerical homogenization of microstructural response using feed-forward as well as convolutional neural networks. Next is surrogate optimization using multifidelity learning for problems of phase evolution. Graph theory bears many equivalences to partial differential equations in its properties of representation and avenues for analysis as well as reduced-order descriptions--all ideas that offer fruitful opportunities for exploration. Neural networks, by their capacity for representation of high-dimensional functions, are powerful for scale bridging in physics--an idea on which we present a particular perspective in the context of alloys. One of the most compelling ideas in scientific machine learning is the identification of governing equations from dynamical data--another topic that we explore from the viewpoint of partial differential equations encoding mechanisms. This is followed by an examination of approaches to replace traditional, discretization-based solvers of partial differential equations with deterministic and probabilistic neural networks that generalize across boundary value problems. The monograph closes with a brief outlook on current emerging ideas in scientific machine learning.
Machine Learning Proceedings 1989
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Author : Alberto Maria Segre
language : en
Publisher: Morgan Kaufmann
Release Date : 2014-06-28
Machine Learning Proceedings 1989 written by Alberto Maria Segre and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-28 with Computers categories.
Machine Learning Proceedings 1989
31st European Symposium On Computer Aided Process Engineering
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Author : Metin Türkay
language : en
Publisher: Elsevier
Release Date : 2021-07-22
31st European Symposium On Computer Aided Process Engineering written by Metin Türkay and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-22 with Technology & Engineering categories.
The 31st European Symposium on Computer Aided Process Engineering: ESCAPE-31, Volume 50 contains the papers presented at the 31st European Symposium of Computer Aided Process Engineering (ESCAPE) event held in Istanbul, Turkey. It is a valuable resource for chemical engineers, chemical process engineers, researchers in industry and academia, students and consultants in the chemical industries. - Presents findings and discussions from the 31st European Symposium of Computer Aided Process Engineering (ESCAPE) event
32nd European Symposium On Computer Aided Process Engineering
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Author : Ludovic Montastruc
language : en
Publisher: Elsevier
Release Date : 2022-06-30
32nd European Symposium On Computer Aided Process Engineering written by Ludovic Montastruc and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-30 with Technology & Engineering categories.
32nd European Symposium on Computer Aided Process Engineering: ESCAPE-32 contains the papers presented at the 32nd European Symposium of Computer Aided Process Engineering (ESCAPE) event held in Toulouse, France. It is a valuable resource for chemical engineers, chemical process engineers, researchers in industry and academia, students and consultants for chemical industries who work in process development and design. - Presents findings and discussions from the 32nd European Symposium of Computer Aided Process Engineering (ESCAPE) event
Deep Learning From First Principles Second Edition
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Author : Tinniam V. Ganesh
language : en
Publisher:
Release Date : 2018-12-13
Deep Learning From First Principles Second Edition written by Tinniam V. Ganesh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-13 with categories.
This is the second edition of the book. The code has been formatted with fixed with a fixed width font, and includes line numbering. This book derives and builds a multi-layer, multi-unit Deep Learning from the basics. The first chapter starts with the derivation and implementation of Logistic Regression as a Neural Network. This followed by building a generic L-Layer Deep Learning Network which performs binary classification. This Deep Learning network is then enhanced to handle multi-class classification along with the necessary derivations for the Jacobian of softmax and cross-entropy loss. Further chapters include different initialization types, regularization methods (L2, dropout) followed by gradient descent optimization techniques like Momentum, Rmsprop and Adam. Finally the technique of gradient checking is elaborated and implemented. All the chapters include implementations in vectorized Python, R and Octave. Detailed derivations are included for each critical enhancement to the Deep Learning. By the time you reach the last chapter, the implementation includes fully functional L-Layer Deep Learning with all the bells and whistles in vectorized Python, R and Octave. The code, for all the chapters, has been included in the Appendix section
Deep Learning From First Principles
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Author : Tinniam V. Ganesh
language : en
Publisher:
Release Date : 2018-05-16
Deep Learning From First Principles written by Tinniam V. Ganesh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-16 with categories.
This book derives and builds a multi-layer, multi-unit Deep Learning from the basics. The first chapter starts with the derivation and implementation of Logistic Regression as a Neural Network. This followed by building a generic L-Layer Deep Learning Network which performs binary classification. This Deep Learning network is then enhanced to handle multi-class classification along with the necessary derivations for the Jacobian of softmax and cross-entropy loss. Further chapters include different initialization types, regularization methods (L2, dropout) followed by gradient descent optimization techniques like Momentum, Rmsprop and Adam. Finally the technique of gradient checking is elaborated and implemented. All the chapters include implementations in vectorized Python, R and Octave. Detailed derivations are included for each critical enhancement to the Deep Learning. By the time you reach the last chapter, the implementation includes fully functional L-Layer Deep Learning with all the bells and whistles in vectorized Python, R and Octave. The code, for all the chapters, has been included in the Appendix section
Artificial Intelligence In Manufacturing
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Author : Masoud Soroush
language : en
Publisher: Elsevier
Release Date : 2024-01-22
Artificial Intelligence In Manufacturing written by Masoud Soroush and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-22 with Technology & Engineering categories.
Artificial Intelligence in Manufacturing: Concepts and Methods explains the most successful emerging techniques for applying AI to engineering problems. Artificial intelligence is increasingly being applied to all engineering disciplines, producing more insights into how we understand the world and allowing us to create products in new ways. This book unlocks the advantages of this technology for manufacturing by drawing on work by leading researchers who have successfully developed methods that can apply to a range of engineering applications. The book addresses educational challenges needed for widespread implementation of AI and also provides detailed technical instructions for the implementation of AI methods. Drawing on research in computer science, physics and a range of engineering disciplines, this book tackles the interdisciplinary challenges of the subject to introduce new thinking to important manufacturing problems. - Presents AI concepts from the computer science field using language and examples designed to inspire engineering graduates - Provides worked examples throughout to help readers fully engage with the methods described - Includes concepts that are supported by definitions for key terms and chapter summaries