Mono, an open source implementation

The .NET framework for use on Linux, Unix and Windows System, has gone beta!

Comments

Post a Comment

Popular posts from this blog

Imote2 with Camera setup IMB400 over Ubuntu 9.10/10.04

Imote2 Camera driver for TinyOS is available in contribution directory of  TinyOS 2.x . It goes by the name of  tinyos-2.x-contrib . Its location within is  tinyos-2.x-contrib/intelmote2/apps I am assuming that you have tinyos-2.1.1 installed, this is required to have Camera Driver build. If you dont have it installed... than better have it done by invoking: apt-get install tinyos-2.1.1 From here on to get the driver working,  the procedure is as follows: Browse to opt directory. cd /opt The recommended way to get the source of tinyos-2.x and tinyos-2.x-contribis is through CVS. echo "Getting TinyOS 2.x and TinyOS 2.x-contrib from CVS" echo "Press enter when asked for password." cvs -d:pserver:anonymous@tinyos.cvs.sourceforge.net:/cvsroot/tinyos login cvs -z3 -d:pserver:anonymous@tinyos.cvs.sourceforge.net:/cvsroot/tinyos co -P tinyos-2.x cvs -z3 -d:pserver:anonymous@tinyos.cvs.sourceforge.net:/cvsroot/tinyos co -P tinyos-2.x-contrib --OR--  e...
Read more

Artificial Intelligence - Weak AI vs Strong AI

Source:  http://www.faculty.umb.edu/gary_zabel/Courses/Bodies,%20Souls,%20and%20Robots/Texts/Artificial%20Intelligence%20.htm Artificial Intelligence As a theory in the philosophy of mind, artificial intelligence (or AI) is the view that human cognitive mental states can be duplicated in computing machinery. Accordingly, an intelligent system is nothing but an information processing system. Discussions of AI commonly draw a distinction between weak and strong AI. Weak AI holds that suitably programmed machines can simulate human cognition. Strong AI, by contrast, maintains that suitably programmed machines are capable of cognitive mental states. The weak claim is unproblematic, since a machine which merely simulates human cognition need not have conscious mental states. It is the strong claim, though, that has generated the most discussion, since this does entail that a computer can have cognitive mental states. In addition to the weak/strong distinction, it is also ...
Read more

Tensorflow - Simplest Gradient Descent using GradientDescentOptimizer

Below is the code to find the minimum possible value of weight (w) such that the outcome of equation is closest to 0, using Gradient Descent method. These weights are multiplied with input (x) to compute cost, thus minimizing it. The equation is below... w 2 - 10 w + 25 ---------------------- (i) i.e. ( w - 5 ) 2 Thus, the coefficients from (i) would be 1, -10 and 25 import numpy as np import tensorflow as tf coefficients = np.array([[ 1 ], [ - 10 ], [ 25 ]]) # try 1, -20, 100 -- line 4 w = tf.Variable([ 0 ], dtype = tf.float32) x = tf.placeholder(tf.float32, [ 3 , 1 ]) cost = (x[ 0 ][ 0 ] * w ** 2 ) + (x[ 1 ][ 0 ] * w) + x[ 2 ][ 0 ] # (w-5)^2 train = tf.train.GradientDescentOptimizer( 0.01 ).minimize(cost) init = tf.global_variables_initializer() with tf.Session() as session: session.run(init) print (session.run(w)) for i in range ( 1000 ): session.run(train, feed_dict = {x:coefficients}) print (session.run(w)) T...
Read more