Batch Gradient Descent

I happened to stumble on Prof. Andrew Ng’s Machine Learning classes which are available online as part of Stanford Center for Professional Development. The first lecture in the series discuss the topic of fitting parameters for a given data set using linear regression.  For understanding this concept, I chose to take data from the top…

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GATE-2012 ECE Q11 (signals)

Question 11 on signals from GATE (Graduate Aptitude Test in Engineering) 2012 Electronics and Communication Engineering paper. Q11. The unilateral Laplace transform of is . The unilateral Laplace transform ofis (A)  (B)  (C)  (D)  Solution From the definition of Laplace transform for a function defined for all real numbers  is,  , where  with real numbers  and . To find the Laplace…

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Stochastic Gradient Descent

For curve fitting using linear regression, there exists a minor variant of Batch Gradient Descent algorithm, called Stochastic Gradient Descent. In the Batch Gradient Descent, the parameter vector  is updated as, . (loop over all elements of training set in one iteration) For Stochastic Gradient Descent, the vector gets updated as, at each iteration the…

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Bit Error Rate (BER) for frequency shift keying with coherent demodulation

Following the request by Siti Naimah, this post discuss the bit error probability for coherent demodulation of binary Frequency Shift Keying (BFSK) along with a small Matlab code snippet. Using the definition provided in Sec 4.4.4 of [DIG-COMM-SKLAR]), in binary Frequency shift keying (BFSK), the bits 0’s and 1’s are represented by signals and having…

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MIMO with MMSE SIC and optimal ordering

This post attempts to build further on the MIMO equalization schemes which we have discussed – (a) Minimum Mean Square Error (MMSE) equalization, (b) Zero Forcing equalization with Successive Interference Cancellation (ZF-SIC) and (c) ZF-SIC with optimal ordering. We have learned that successive interference cancellation with optimal ordering improves the performance with Zero Forcing equalization….

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Closed form solution for linear regression

In the previous post on Batch Gradient Descent and Stochastic Gradient Descent, we looked at two iterative methods for finding the parameter vector  which minimizes the square of the error between the predicted value  and the actual output  for all  values in the training set. A closed form solution for finding the parameter vector  is possible, and in this post…

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Solved!

SOLVED the Rubik’s cube !!!   After 6 months, 2 cube’s and countless twists and turns, extremely glad to reach here. Will enjoy the beauty of the solved cube for couple of days before breaking it and going over the whole journey again…. (Thanks dear Kunju for introducing me to the cube) Disclosure : After solving…

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