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DaFeda's Blog | Mathematics; ranting & learningMathematics; ranting & learning
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Mathematics; ranting & learning
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DaFeda's Blog | Mathematics; ranting & learning | dafeda.wordpress.com Reviews
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Mathematics; ranting & learning
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.
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...
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...
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...
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:...
theclevermachine.wordpress.com
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...
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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Kurs, 20. mart. 28 februar 2018. od 18 do 22h. Amfiteatar centralne zgrade Univerziteta u Novom Sadu (Dr Zorana Đinđića bb). Uspešno ste se prijavili za DaFED. Wooohooo! We’re all I hope! Je neprofitna organizacija posvećena umrežavanju i organizovanju edukativnih događaja za IT zajednicu. Prvi DaFED meetup je održan u junu 2012. spajajući ljude iz sveta grafičkog i veb dizajna, veb developmenta i veb marketinga i od tada se održava svakog meseca. Sem u januaru) u Novom Sadu. Želiš da budeš deo DaFED-a?
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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Posted 10 months ago. Posted 1 year ago. Posted 1 year ago. Technical Exercise #338 - Serenading myself to Tuesday. Neat what a little dabbling for a lot of years can yield. Posted 1 year ago. Technical Exercise #338 - Serenading myself to Tuesday. Neat what a little dabbling for a lot of years can yield. Posted 1 year ago. Posted 1 year ago. Losing myself in magical places. #latergram. Posted 2 years ago. Posted 2 years ago. Posted 2 years ago. Posted 2 years ago. I'm David, and I like to create.
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