Joint and Marginal Probability Mass Function For UploadingIf (X,Y) is a two-dimensional discrete random variable, then joint probability mass function of X and Y denoted by pxy and is defined as pxy(xi,yj)=P(X=xi,Y=yj) If you toss three coins the following sample space you will get. S={TTT, TTH, THT, THH, HTT, HTH, HHT,HHH} X—- Occurrence of heads […]
Joint and Marginal Probability Mass Function Read More »
1-Random Experiment- If an experiment is conducted then the outcome will not be unique but it may be from possible list of outcomes. Example- If you are tossing two coins the possible outcomes would be {TT, TH, HT,HH}. 2- Events- Outcome of a random experiment is called events, if you are tossing two coins then
Terminologies Related to Probability Theory Read More »
Entropy of a random variable measures uncertainty inherent to possible outcomes of the variable. H(X)= -Σin pilog2pi , where i=1, 2, 3 , 4, …..,n Where pi are probabilities of associated events. Take an example of tossing two coins, then you will have sample space . S={T,H} And X is defined as number of
Entropy: A Measure of Uncertainty Read More »
A random variable X is a function from a sample S to real numbers R, i.e X: S——>R. Suppose you are tossing three coins then the sample space would be. S={HHH,TTH, THT, THH, HTT, HTH, HHT, HHH} Then X maps sample space to real numbers. Where you can see that R={0,1,2, 3} You can also
Expectation of a Discrete Random Variable Read More »
A random variable X is a function from a sample S to real numbers R, i.e X: S——>R. Suppose you are tossing three coins then the sample space would be. S={HHH,TTH, THT, THH, HTT, HTH, HHT, HHH} Then X maps sample space to real numbers. Where you can see that R={0,1,2, 3} You can also
Random Variable and Probability Density Function Read More »
Taylor’s theorem states that we can get expansion of a function f(x) at x=a This can be written as When we put a=0 it becomes Maclaurin’s series, and it has a lot of applications in mathematics and other areas. If you want expansion of ex you will require to find f(0), f’(0), f’’(0), f’’’(0), ………….,
Taylor’s and Maclaurin’s Series Read More »
In this post, I am going to explain how to attach a Servo motor with Arduino. I will explain about programming and how to connect Servo motor with Arduino. You will require to install Servo library for functioning of Servo motor #include <Servo.h> Servo s; int i = 0; void setup() { s.attach(9); } void
Servo Motor with Arduino Read More »
Keras Deep learning framwork is very popular among researchers and developers which is built at top of Tensorflow. Furthermore, Keras is very handy and easy to use, creating layers and connections is matter of few lines of code. In this post, I am going to explain, how you can train a simple Deep learning network
Train a Simple Deep Learning Network using Keras and Iris DataSet Read More »
In this post, we are going make a program which will show three names at delay of 10 seconds using Arduino microcontroller and Liquid Crystal Display. Moreover, you will require two libraries Wire and LiquidCrystal_I2C . Again LiquidCrystal_I2C lcd(0x27,16,2); line sets LCD address at 0x27 having 16 columns and 2 rows. In the below program
Display Three Names at Delay of 10 Seconds using Arduino and LCD Read More »
Linear regression Linear regression is a supervised learning algorithm and tries to learn a linear function from a given data set D={(xi, yi)} where i=1, 2, 3, 4,———–N where xi is feature vector and yi is target output. In other words, you will try to find out relationship between xi and yi. Suppose you want to
Linear Regression using Gradient Decent Read More »