Deep Neural Networks in a Mathematical Framework

Deep Neural Networks in a Mathematical F

📁 Deep Neural Networks in a Mathematical Framework

✍ Anthony L. Caterini; Dong Eui Chang 📂 Library 📅 2018 🏛 Springer 🌐 English

This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. The authors provide tools to represent and describe neural networks, casting previous results in the field in a more natural light. In particular, the authors derive gradient descent algo

Deep Neural Networks in a Mathematical F

📁 Deep Neural Networks in a Mathematical Framework (SpringerBriefs in Computer Science)

✍ Anthony L. Caterini, Dong Eui Chang 📂 Library 📅 2018 🏛 Springer 🌐 English

<p><span>This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. The authors provide tools to represent and describe neural networks, casting previous results in the field in a more natural light. In particular, the authors derive gradient des

Neural Networks in a Softcomputing Frame

📁 Neural Networks in a Softcomputing Framework

✍ K. -L. Du PhD, M. N. S. Swamy PhD, D.Sc (Eng) (auth.) 📂 Library 📅 2006 🏛 Springer-Verlag London 🌐 English

<p><P>Conventional model-based data processing methods are computationally expensive and require experts’ knowledge for the modelling of a system; neural networks provide a model-free, adaptive, parallel-processing solution. <EM>Neural Networks in a Softcomputing Framework </EM>presents a thorough r

Neural Networks in a Softcomputing Frame

📁 Neural Networks in a Softcomputing Framework

✍ Ke-Lin Du, M.N.S. Swamy 📂 Library 📅 2006 🏛 Springer 🌐 English

Conventional model-based data processing methods are computationally expensive and require experts knowledge for the modelling of a system; neural networks provide a model-free, adaptive, parallel-processing solution. Neural Networks in a Softcomputing Framework presents a thorough review of the mos

Hands-On Mathematics for Deep Learning:

📁 Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks

✍ Jay Dawani 📂 Library 📅 2020 🏛 Packt Publishing 🌐 English

<p><b>A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures</b></p> <h4>Key Features</h4> <ul><li>Understand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networks </li> <li

Hands-On Mathematics for Deep Learning:

📁 Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks

✍ Jay Dawani 📂 Library 📅 2020 🏛 Packt Publishing 🌐 English

<p><b>A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures</b></p> <h4>Key Features</h4> <ul><li>Understand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networks </li> <li