Description: 书籍“Regularization tools for training large feed-forward neural networks using Automatic Differentiation”的源码文件 Platform: |
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Author:mjj |
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Description: 书籍“Regularization tools for training large feed-forward neural networks using Automatic Differentiation”的源码文件-Books Platform: |
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Author:mjj |
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Description: Semi-automatic Differentiation (SD) Toolkit is a Matlab implementation
of the complex step derivative (CSD) technique for the differentiation of real-valued functions. The Toolkit consists of three core functions:
sdGrad.m - Returns CSD approximation of the gradient (g) of the
scalar-valued target function fun(p,Extra), according to Equation 3
of the paper.
sdJac.m - Returns CSD approximation of the Jacobian (J) of the
scalar-valued target function fun(p,Extra), according to Equation 3
of the paper.
sdHg.m - Rerurns CSD approximation of the Hessian (H) of the
scalar-valued target function fun(p,Extra), according to Equation 7 of the
paper. It also returns the centered-difference CSD approximation of the
gradient as a by-product.
For a brief describtion of the functions in the toolkit,
type ,help sdToolkit> at Matlab command prompt.-The sdToolkit demonstrates the complex step derivative method on a variety of functions and geophysically oriented examples Platform: |
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Author:蒋礼 |
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Description: Numerical Optimization-Every year optimization algorithms are being called on to handle problems that are much larger and complex than in the past. Accordingly, the book emphasizes large- scale optimization techniques, such as interior-point methods, inexact Newton methods, limited-memory methods, and the role of partially separable functions and automatic differentiation. It treats important topics such as trust-region methods and sequential quadratic programming more thoroughly than existing texts, and includes comprehensive discussion of such “core curriculum” topics as constrained optimization theory, Newton and quasi-Newton methods, nonlinear least squares and nonlinear equations, the simplex method, and penalty and barrier methods for nonlinear programming. Platform: |
Size: 5659648 |
Author:Nu |
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Description: 利用自动微分技术求解高阶导数的实用工具，可用来求解非线性系统的各种高阶矩阵，亲证可用。-Use of automatic differentiation to solve the higher order derivatives of utility can be used to solve nonlinear systems of a variety of high-end matrix, pro-card available. Platform: |
Size: 13100032 |
Author:刘涛 |
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Description: 自动微分法源程序，一般用于基于梯度优化过程中的梯度求解。-Source of automatic differentiation method, generally used for solving based on the gradient gradient optimization process. Platform: |
Size: 5568512 |
Author:杨晓东 |
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Description: This a suite of tools to solve automatic numerical differentiation problems in one or more variables. All of these methods also produce error estimates on the result.
A pdf file is also provided to explain the theory behind these tools.Author: John D Errico
e-mail: woodchips@rochester.rr.com
Release: 1.0
Release date: 3/7/2007-This is a suite of tools to solve automatic numerical differentiation problems in one or more variables. All of these methods also produce error estimates on the result.
A pdf file is also provided to explain the theory behind these tools.Author: John D Errico
e-mail: woodchips@rochester.rr.com
Release: 1.0
Release date: 3/7/2007
Platform: |
Size: 164864 |
Author:Jane |
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Description: Explore machine learning concepts using the latest numerical computing library — TensorFlow — with the
help of this comprehensive cookbook(TensorFlow was open sourced in November of 2015 by Google, and since then it has become
the most starred machine learning repository on GitHub. TensorFlow's popularity is due to the
approach of creating computational graphs, automatic differentiation, and customizability.
Because of these features, TensorFlow is a very powerful and adaptable tool that can be used
to solve many different machine learning problems.) Platform: |
Size: 3738624 |
Author:liming123a
|
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