Java code comprise of class and variable. Class and Variable come with different scope(private/public/static). All this takes memory. JVM has been given responsibility to run the java code. JVM gives memory for class/variables. But how does JVM manages memory? Let's imagine JVM memory as big box as it is shown in the picture below. We will... Continue Reading →
Introduction to Machine Learning Terminology and Perceptron
We talked about introduction to machine learning here. Let's say, we have two armies: red and blue. The black line is the border separating these two armies. The line is curved and it is drawn using visual inspection. But the kings are mad and they demand a straight line, not a curved one. To please... Continue Reading →
int vs Integer – Java Application Memory Usage
Before you read further, Please answer the following question: A. int a = 100; B. Integer b = new Integer(100); There are two java statements written above. What is the ratio of memory used by b to memory used by a in a 32 bit machine? The four options are: 1 1.5 2 4... Continue Reading →
An Introduction To Machine Learning
Machine Learning is a kind of buzz words these days; sort of a mysterious new guy in the class whom everyone wants to be friend with. On the onset, I would like to think Machine Learning as a part of tool kit of every Software Engineer. And conceptually it's not different from the Mathematics, Algorithm,... Continue Reading →
A tutorial To Find Best Scikit classifiers For Sentiment Analysis
An introduction to sentiment analysis comparing different classifiers using scikit, nltk and panda.
A Tutorial to Understand Decision Tree ID3 Learning Algorithm
Introduction Decision Tree learning is used to approximate discrete valued target functions, in which the learned function is approximated by Decision Tree. To imagine, think of decision tree as if or else rules where each if-else condition leads to certain answer at the end. You might have seen many online games which asks several question and lead... Continue Reading →
Entropy In Machine Learning
Entropy is a measure of randomness. In other words, its a measure of unpredictability. Let's take an example of a coin toss. Suppose we tossed a coin 4 times, and the output of the events came as {Head, Tail, Tail, Head}. Based solely on this observation, if you have to guess what will be the... Continue Reading →
A Beginner Tutorial For ML Decision Tree Model Using Scikit And Panda
Prerequisite Python, Scikit and Panda installed in your laptop. It's better to install conda as it has all the required libraries. Install Jupyter too. It really helps in python coding. Panda Panda is a popular python library to explore and manipulate data. Scikit Scikit is popular machine learning framework in python. Regression Regression is process to... Continue Reading →
Huffman Code – An example of Greedy Algorithm
Introduction A greedy algorithm always makes the choice that looks best at the moment. It makes a locally optimal choice in the hope that this choice will lead to a globally optimal solution. Greedy algorithms do not result in optimal solutions always but for many problems they do. A problem can be solved by Greedy... Continue Reading →
Dynamic Programming : An example showing Lowest Common Subsequence in Java
Introduction Optimal Substructure : A problem is said to be a optimal substructure if an optimal solution can be constructed from optimal solution of it's subproblem. Overlapping subproblems : A problem is said to have overlapping subproblems if the problem can be broken down into repetitive subproblems. Greedy Algorithm is used to solve problems having optimal... Continue Reading →