I have been experimenting with various methods to synchronize my personal projects with my home PC and my laptop. Exporting, importing, making changes, and again exporting........this has been a real headache especially the huge mess it creates with all the dependency issues that I have to resolve every time. Of course the use of version controlling would be best solution as many of the developers out there would agree. But still the private repositories are not for free (eg GitHub), so it is out of my scope since I still cannot afford one :'(
Here's my most 'recent' solution and the probably the best so far, ie. maintaining a git remote repository in a file hosting service such as Dropbox or Google Drive. I'm a beginner to learning Git. At the beginning I did some silly mistakes like using Git clients which made learning git far more complicated; I still hate EGit - the eclipse default git client :-@ but once I switched to good old fashioned command line it all made sense.
JSP Basics
December 30, 2014
Java Server Pages or JSPs are very similar to typical html pages except they are embedded with java code. This simplifies the development of dynamic web applications.
How JSPs are related to Servlets
JSPs are basically an abstraction to Servlets. Before compilation, JSPs are translated into Servlets and then compiled by the servlet container.JSP Lifecycle
- JSP is translated into a Servlet (creating equivalent Java source code for a servlet) and Compiled by the servlet container.
- An instance of the corresponding Servlet class is created and initialized by the by calling jspInit() method.
- This servlet object stays in the memory and for each request _jspService() method is invoked, passing the request and response objects.
- When the container needs to remove the servlet instance from service jspDestroy() method is called.
JSP Syntax
Introduction to Linear Regression: Normal Equation
November 1, 2014
Normal Equation (Method II)
The use of Normal Equation to solve for the optimal \(\ \theta\) and find the hypothesis function is an alternate method to what we talked about in the last post and probably the easiest, but of course they have their own benefits and drawbacks.Here we don't need to define a cost function, therefore we don't need to select \(\ \alpha \); the learning rate or specify number of iterations. Same as before, X is our feature matrix and for the intercept term \(\ \theta_0\), a column of ones should be added to X before calculation.
Labels:
Linear Regression,
Machine Learning
Introduction to Linear Regression: Cost Function
October 29, 2014
Cost Function (Method I)
For calculating the cost in linear regression, typically we use Sum of Squared Error method(SSE)
The goal is to minimize \(\ J(\theta)\) and figure out \(\ \theta\) values corresponding to the minimum cost. There are several optimization algorithms used to achieve this.
\(\ J(\theta) = \frac{1}{2m}\sum_{i=1}^m{(h(x^{(i)})-{y^{(i)}})^2}\)
The goal is to minimize \(\ J(\theta)\) and figure out \(\ \theta\) values corresponding to the minimum cost. There are several optimization algorithms used to achieve this.
Labels:
Linear Regression,
Machine Learning
Introduction to Linear Regression: Polynomial Regression
October 26, 2014
Polynomial Regression
This form of regression is used to fit more complex functions and this is a general concept not restricted to linear regression, but also used commonly in classification algorithms such as Logistic Regression and Neural Networks as well. I hope to talk about it in detail in a future post. For now take a look at the following scatter plot.
Labels:
Linear Regression,
Machine Learning
Introduction to Linear Regression: A Machine Learning Approach
October 7, 2014
Supervised Learning is a form of learning in which we use known data with actual outputs from past experiences to model a relationship and this model is used to predict future outcomes. The known data used to build up the model is called 'training data'.
To build a supervised learning model we need,
- Training Data
- Hypothesis
- Cost Function
- Optimization method for Minimization
Labels:
Linear Regression,
Machine Learning
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