Machine Learning (ML)

Eigenfaces for Face recognition

In this article, we have explored EigenFaces in depth and how it can be used for Face recognition and developed a Python demo using OpenCV for it.

KL Sum algorithm for text summarization

Kullback-Lieber (KL) Sum algorithm for text summarization which focuses on minimization of summary vocabulary by checking the divergence from the input vocabulary.

Machine Learning (ML)

Image Recognition using Transfer Learning Approach

In this post, we will explore Transfer Learning and see what exactly it is and how it works along with a Python implementation for the image recognition tasks.

Machine Learning (ML)

LBPH algorithm for Face Recognition

In this article, we will explore the Local Binary Patterns Histogram algorithm (LBPH) for face recognition. It is based on local binary operator and is one of the best performing texture descriptor.

Machine Learning (ML)

Face Recognition using Fisherfaces

In this article, we will explore FisherFaces techniques of Face Recognition. FisherFaces is an improvement over EigenFaces and uses Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).

Machine Learning (ML)

Latent Semantic Analysis for text summarization

Latent Semantic Analysis is an efficient technique for text summarization in order to abstract out the hidden context of the document.

Machine Learning (ML)

Random Forests using Scikit-learn

In this article, we will implement random forest in Python using Scikit-learn (sklearn). Random forest is an ensemble learning algorithm which means it uses many algorithms together

Machine Learning (ML)

Decision Trees using Scikit-learn

In this article, we will understand decision tree by implementing an example in Python using the Sklearn package (Scikit Learn).

Machine Learning (ML)

SumBasic algorithm for text summarization

SumBasic is an algorithm to generate multi-document text summaries. Basic idea is to utilize frequently occuring words in a document than the less frequent words so as to generate a summary

Machine Learning (ML)

Deep Q-Learning: Combining Deep Learning and Q-Learning

The idea in deep Q networks is that the states and possible outcomes in Q-Learning is replaced with a neural network which tries to approximate Q Values. It is referred to as the approximator

Machine Learning (ML)

SMOTE for Imbalanced Dataset

In this post, we will see how to deal with an imbalanced dataset using SMOTE (Synthetic Minority Over-sampling TEchnique). We will also see its implementation in Python.

Machine Learning (ML)

Object Detection using Region-based Convolutional Neural Networks (R-CNN)

In this post, we will look at Region-based Convolutional Neural Networks (R-CNN) and how it used for object detection. We'll see why the R-CNN came into the picture when CNNs were already into existence.

Machine Learning (ML)

Text Summarization Techniques

Text Summarization is the process of creating a compact yet accurate summary of text documents. In this article, we will cover the different text summarization techniques.

Machine Learning (ML)

Text Preprocessing Techniques

In this post, we will look at a variety of text preprocessing techniques which are frequently used for a Natural language processing (NLP) task.

Software Engineering

MLIR: Redefining the compiler infrastructure

MLIR (Multi-level intermediate representation) is an intermediate representation system between a language or library and the compiler backend (like LLVM)

Machine Learning (ML)

Intuitive Introduction to Gaussian Processes

Gaussian Process is a non-parametric model that can be used to represent a distribution over functions.

Machine Learning (ML)

Understand Neural Networks intuitively

Neural Networks act as a ‘black box’ that takes inputs and predicts an output and it learns complex non-linear mappings to produce far more accurate output classification results.

Machine Learning (ML)

Logistic Regression using Scikit Learn

In this article, we will explore how to implement Logistic Regression in Python using Scikit Learn and create a real demo.

Machine Learning (ML)

Linear regression in Python using Scikit Learn

In this article, we will implement linear regression in Python using scikit-learn and create a real demo and get insights from the results.

Machine Learning (ML)

Hyper Parameter Tuning

Hyperparameter tuning is one of the features that come to the fore to conquer the battle of maximizing the performance of the model or maximizing the model's predictive accuracy.

Software Engineering

Developing a Live Sketching app using OpenCV and Python

We will develop an application which will show a live sketch of your webcam feed. In this project we'll be using NumPy and OpenCV

Machine Learning (ML)

Various Techniques used for Face Recognition

Did you know that everytime we upload an image to a site like Facebook they use facial recognition to recognize faces in it? Learn its various techniques

Machine Learning (ML)

Introduction to Q Learning and Reinforcement Learning

Read on to learn the basics of reinforcement learning and Q-Learning through an intuitive explanation, and a TensorFlow implementation!

Machine Learning (ML)

K nearest neighbors (KNN) algorithm

K nearest neighbors (K-NN) is an algorithm which is used for classification and regression and is based on the idea of considering the nearest K data points for calculations

Machine Learning (ML)

Beginner's Guide to Generative Adversarial Networks with a demo

Generative Adversarial Network is a network with two opposite components which train to eventually reach the target. This was developed in 2014.