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DaFeda's Blog | Mathematics; ranting & learning

Mathematics; ranting & learning

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DaFeda's Blog | Mathematics; ranting & learning | dafeda.wordpress.com Reviews
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DaFeda's Blog | Mathematics; ranting & learning | dafeda.wordpress.com Reviews

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Mathematics; ranting &amp; learning

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Lagrangian and Eulerian coordinate systems | DaFeda's Blog

https://dafeda.wordpress.com/2012/02/11/lagrangian-and-eulerian-coordinate-systems

Mathematics; ranting and learning. Lagrangian and Eulerian coordinate systems. February 11, 2012 in expository articles. I’ve written a 5-page expository article on the Lagrangian and Eulerian coordinate system, where the intuition behind the systems is discussed as well as the math that follows. The article also has a couple of nice figures courtesy of me playing around with Inkscape. Edit (22.03.2012) : Spotted some errors, changed wording. In Theory – Latex to Wordpress. Comments feed for this article.

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1D Diffusion equation (Heat equation) | DaFeda's Blog

https://dafeda.wordpress.com/2011/06/09/1d-diffusion-equation-heat-equation

Mathematics; ranting and learning. 1D Diffusion equation (Heat equation). June 9, 2011 in expository articles. According to the 12-step program. It’s time to solve a 1D diffusion equation, aka Heat equation of the form,. Is the viscosity. The process is very similar to solving a 1D convection equation. But this time we need to approximate a second derivative. An easy way to get an expression that approximates a second derivative is by using Taylor series:. Now add them up and truncate to get,. March 5, 2...

3

Running the source code from Jos Stams paper “Real-Time Fluid Dynamics for Games” | DaFeda's Blog

https://dafeda.wordpress.com/2011/12/27/running-the-source-code-from-jos-stams-paper-real-time-fluid-dynamics-for-games

Mathematics; ranting and learning. Running the source code from Jos Stams paper “Real-Time Fluid Dynamics for Games”. December 27, 2011 in Uncategorized. The purpose of this post is to explain how to compile the code explained in the paper “Real-Time Fluid Dynamics for Games” by Jos Stam. To download the paper. To download code. The complete code consists of two files, source.c and demo.c. They are zipped in one file code.zip. Gcc -c -o demo.o demo.c -I”D: Dropbox Code GLUT include”. The folder where you...

4

daFeda | DaFeda's Blog

https://dafeda.wordpress.com/author/dafeda

Mathematics; ranting and learning. You are currently browsing daFeda’s articles. Derivation: Error Backpropagation and Gradient Descent for Neural Networks. March 31, 2015 in Uncategorized. The easiest to follow derivation of backpropagation I’ve come across. And try to provide some high-level insights into the computations being performed during learning. Figure 1: Diagram of an artificial neural network with one hidden layer. Some Background and Notation. 1,990 more words. To download the paper. To dow...

5

Derivation: Error Backpropagation & Gradient Descent for Neural Networks | DaFeda's Blog

https://dafeda.wordpress.com/2015/03/31/derivation-error-backpropagation-gradient-descent-for-neural-networks

Mathematics; ranting and learning. Derivation: Error Backpropagation and Gradient Descent for Neural Networks. March 31, 2015 in Uncategorized. The easiest to follow derivation of backpropagation I’ve come across. And try to provide some high-level insights into the computations being performed during learning. Figure 1: Diagram of an artificial neural network with one hidden layer. Some Background and Notation. 1,990 more words. In Theory – Latex to Wordpress. My solutions to problems from various books.

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Root Finding Method | Numerical Analysis

https://scondemth361.wordpress.com/root-finding-method

Just another WordPress.com weblog. A root finding algorithm is a numerical method of finding an. For a given function. This particular value of. Is the root. There are many different methods of finding roots of a function. Below are sum of them. Bisection method is a simple root finding method. It requires prior knowledge of the value of a continuous function. At the endpoints of a given interval. Where the root exist. The function value at the endpoints must have different signs (i.e. If Fleft*Fright 0:...

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Derivation: Error Backpropagation & Gradient Descent for Neural Networks | The Clever Machine

https://theclevermachine.wordpress.com/2014/09/06/derivation-error-backpropagation-gradient-descent-for-neural-networks

Skip to main content. Skip to primary sidebar. Skip to secondary sidebar. Topics in Computational Neuroscience and Machine Learning. About The Clever Machine. Larr; Model Selection: Underfitting, Overfitting, and the Bias-Variance Tradeoff. Derivation: Derivatives for Common Neural Network Activation Functions →. Derivation: Error Backpropagation and Gradient Descent for Neural Networks. And try to provide some high-level insights into the computations being performed during learning. Are multiplied by t...

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Using LaTeX2WP | in theory

https://lucatrevisan.wordpress.com/latex-to-wordpress/using-latex2wp

Marge, I agree with you – in theory. In theory, communism works. In theory. — Homer Simpson. Skip to primary content. The distribution contains the following files:. Specifies typesetting options, and environments and macros recognized during the conversion. Some basic information on how to use LaTeX2WP. Sets the typesetting options in the style of Terry Tao’s blog. A sample post showing how to use the main functionalities recognized by LaTeX2WP. The text of the GNU General Public License. It is possible...

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DaFeda's Blog | Mathematics; ranting & learning

Mathematics; ranting and learning. Derivation: Error Backpropagation and Gradient Descent for Neural Networks. March 31, 2015 in Uncategorized. The easiest to follow derivation of backpropagation I’ve come across. Originally posted on The Clever Machine. And try to provide some high-level insights into the computations being performed during learning. Figure 1: Diagram of an artificial neural network with one hidden layer. Some Background and Notation. 1,990 more words. December 27, 2011 in Uncategorized.

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