## Installing Libraries Anaconda

Now we will install the python libraries in anaconda. We will install “numpy” and pandas and we will also check the version of the libraries installed. For this, we need to open the anaconda navigator. If you remember we have created a new file named as “new2” (you may have created by some other name) Make sure in the application tab you have selected the file which you have created earlier as in my case its named as new2 also circled with a red circle. Next, we are going to…

## Machine Learning with Python

Machine learning is an area of interest for many people for almost 10 years. Many people have researched in this area tremendously for the last decade. Today we are going to start Machine Learning with Python. If you have no earlier experience of machine feel free to follow along. The software we are going to use is anaconda which can be downloaded from this link https://www.anaconda.com/distribution/. The python version we are going to use is Python 3.7. So download the version of anaconda 32bit or 64 bit based on your…

## Enhancing the life of Lead Acid Battery

The life of the lead-acid batteries heavily depends on the rate of charging and discharging. We can increase the life of our batteries by simply controlling the charging and discharging rate.  In this article, you will find about the optimal value of charging and discharging rate of a battery. SOC is defined as the state of charge of the battery. This parameter is generally used to design the controller for the battery. It will depict the percentage of battery charge at the instantaneous time. Batteries come with instructions to keep…

## Convolutions with Open-CV and Python:

Convolution is one of the most critical and fundamental building block in computer vision. In simple words it’s an element wise multiplication of the matrices and then summed. In simple words Multiply the two matrices element by element Sum the elements together In terms of computer vision suppose we have a 2 dimensional image with x number of rows and y number of columns. We have another matrix which may be smaller or tiny in size called as kernel matrix. It is normal to find hard defined kernel matrices for…

## Supervised Learning

Supervised learning can be expressed in real life as Learning Under the Supervision of Someone. In machine learning terms we can say that machine is learning based on some data. It is used when we have some sort of input and we want to predict the output based on some dataset. Training data or labeled data is some sort of supervisor for the machine which is giving instructions to the machine. Training data or labeled data has both input and output. Let’s first take an example that we have data…

## Sigmoid Graph (Python Program)

###################### # # # Import Section # # # ###################### from matplotlib import pylab import numpy ##################################### # # # Defining the sigmoid Function # # # ##################################### def sigmoid(x): sigmoid_return = 1 / (1 + numpy.exp(-x)) return sigmoid_return # Linspace is used as array with start and # stop value. In the line below -10 is the # start value and 10 is the stop value # and 10 is the value of total number of # steps with end point included true as # default x = pylab.linspace(-10,10,10)…

## Sigmoid Function

As in the last article we were discussing Perceptron. Now we will discuss the Sigmoid Activation Function. As the name represents its shape will be somewhere similar to sigma σ. The above-mentioned figure is the graph of the sigmoid activation function. It provides the output from 0 to 1. The sigmoid activation function can be described by the following expression First let’s assume that we put three values (0, +∞, -∞) turn by turn in the above-mentioned expression. First, let’s put 0 then the expression will become After putting the…

## Perceptron

In the last article, we were talking about Perceptron, the model proposed before the activation model. It was developed by Frank Rosenblatt back in 1950s-60s. The Perceptron model is very simple in approach. It takes several binary inputs to multiply it with weights and produces a single binary output. The best way to learn is by example. Let’s take an example that you’ll have to go office in the morning but there are certain requirements that must be fulfilled before going to the office. It must be working day because…

## Introduction to Neural Networks

Neural Networks, this name appears to be something very much difficult to understand, something that can only be understandable by some super genius. Well, this is not that much difficult as it is supposed to be. As we know human brains are incredibly genius in pattern recognition. We categorize the objects in the surroundings with little or no effort. The example normally given in pattern recognition is to understand the digits written by some human being. Let’s say we have the digits written below and we have to recognize it…

## KALMAN FILTER (Part 2)

Hello everyone. Hope you all will be fine. Now let’s move to the 2nd block mentioned in the flow chart named as “Calculate the Current Estimate”. The flow chart mentioned in the previous is as follow Actually, there are 3 things are important to be calculated. Kalman Gain (Method to calculate the Kalman Gain) Current Estimate The Error in new Estimate Therefore, there are 3 important equations to calculate the above-mentioned items. For Kalman Gain, the equation is as follow KG = Kalman Gain EEST = Error in Estimate EMEA…