All you need to know about the famous Object Detection algorithm
You may have heard your friend talking about Object Detection and perhaps utter the words like YOLO and Faster-RCNN. If you’ve wondered what YOLO meant and why you should even care about it look no further.
This blog will provide an exhaustive study of YOLOv3 (You only look once, version 3), which is one of the most popular deep learning models extensively used for object detection, semantic segmentation, and image classification.
I’ll explain the architecture of YOLOv3 model, with its different layers, and we’ll see some results for object detection that I got while running the inference program on some…
A brief Introduction
Tensorflow is a deep learning library that makes building and deploying Deep Learning Applications super easy. If you wondered what this library is all about wait no more, keep reading the blog to find what makes Tensorflow unique.
This blog provides an overview to the Tensorflow library and provides a brief introduction to the topic with some important keywords, it’s installation and demo code.
What is TensorFlow ?
- It is a free and open-source platform for high-performance numerical computation, specifically for ML and Deep Learning.
- Has a flexible architecture and can be deployed across a variety of platforms (CPUs, GPUs and TPUs) as well…
Learn the why, what and how of Docker
In this blog we’ll try to understand one of the most popular tools used to containerize and deploy applications over the internet i.e. Docker. It makes deploying applications extremely simple.
We will try to look at the things that make Docker so special and learn how you can build, deploy, and fetch applications using Docker & Docker Hub using just a few steps.
What is Docker ?
- It is a tool used to create, deploy and run applications by using containers.
- Containers allows developers to package up an application with all the parts it needs.
- Containers are isolated from one another and bundle their…
A Brief Overview
Introduction
Support Vector Machines (SVMs) are a set of supervised learning methods which learn from the dataset and can be used for both regression and classification. An SVM is a kind of large-margin classifier: it is a vector space based machine learning method where the goal is to find a decision boundary between two classes that is maximally far from any point in the training data.
Support Vectors :
The term Support Vectors refers to the co-ordinates of individual observation. Support Vector Machine is a frontier which best segregates the two classes using a hyperplane/ line.
and its implementation in Python
In this blog we’ll try to dig deeper into Random Forest Classification. Here we will learn about ensemble learning and will try to implement it using Python.
You can find the code over here.
Random Forest Classifier :
It is an ensemble tree-based learning algorithm. The Random Forest Classifier is a set of decision trees from randomly selected subset of training set. It aggregates the votes from different decision trees to decide the final class of the test object.
Ensemble Algorithm :
Ensemble algorithms are those which combines more than one algorithms of same or different kind for classifying objects. …
Getting started with Logistic Regression theory
Introduction
Logistic Regression is a Supervised learning algorithm widely used for classification. It is used to predict a binary outcome (1/ 0, Yes/ No, True/ False) given a set of independent variables. To represent binary/ categorical outcome, we use dummy variables.
Logistic regression uses an equation as the representation, very much like linear regression. It is not much different from linear regression, except that a Sigmoid function is being fit in the equation of linear regression.
Simple Linear and Multiple Linear Regression Equation:
y = b0 + b1x1 + b2x2 + ... + eSigmoid function :
p = 1 /…An introduction to Decision Tree Classifier
A Decision Tree is a simple representation for classifying examples. It is a Supervised Machine Learning where the data is continuously split according to a certain parameter.
Decision Tree consists of :
- Nodes : Test for the value of a certain attribute.
- Edges/ Branch : Correspond to the outcome of a test and connect to the next node or leaf.
- Leaf nodes : Terminal nodes that predict the outcome (represent class labels or class distribution).
A Brief Introduction
In this blog, we’ll talk about one of the most widely used machine learning algorithms for classification, which is the K-Nearest Neighbors (KNN) algorithm. K-Nearest Neighbor (K-NN) is a simple, easy to understand, versatile and one of the topmost machine learning algorithms that find its applications in a variety of fields.
In this blog we’ll try to understand what is KNN, how it works, some common distance metrics used in KNN, its advantages & disadvantages along with some of its modern applications.
What is K-NN ?
K-NN is a non-parametric and lazy learning algorithm. Non-parametric means there is no assumption for underlying data distribution…
Along with its implementation in Python
In this blog we’ll try to understand one of the most important algorithms in machine learning i.e. Random Forest Algorithm. We will try to look at the things that make Random Forest so special and will try to implement it on a real life dataset.
The Code along with the dataset can be found here.
Ensemble Learning
An Ensemble method is a technique that combines the predictions from multiple machine learning algorithms together to make more accurate predictions than any individual model. A model comprised of many models is called an Ensemble model.
A step by step Approach
We’re going to be implementing Linear Regression on the ‘Boston Housing’ dataset.
The Boston data set contains information about the different houses in Boston. There are 506 samples and 13 feature variables in this dataset. Our aim is to predict the value of prices of the house using the given features.
Importing necessary Libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as pltLoading our dataset into a Data Frame
data = pd.read_csv("boston.csv")To get basic details about our Boston Housing dataset like null values or missing values, data types etc. we can use .info() as shown below:
data.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 506 entries, 0 to 505
Data columns…
