Machine Learning (ML)

Text classification using CNN

In this article, we are going to do text classification on IMDB data-set using Convolutional Neural Networks(CNN). We will go through the basics of Convolutional Neural Networks and how it can be used with text for classification.

Types of Neural Networks

Today, there are over 10 types of Neural Networks and each have a different central idea which makes them unique. We have explored all types in this article

Machine Learning (ML)

Text Generation using LSTM

LSTM are a perfect fit for text generation as text are a sequence of words and can predict the next word. We have explained the basic idea and developed a demo for it

Software Engineering

Approaches for implementing Autocomplete Feature

The various approaches for Text Auto-completion are Linear Search (Brute Force), Binary Search Approach, TRIE Data Structure, Ternary Search Tree Approach, Fuzzy Search, Burkhard Keller Tree Approach and Machine Learning Approach.

Machine Learning (ML)

K-medoids Clustering

K-medoids Clustering is an Unsupervised Clustering algorithm that cluster objects in unlabelled data. It is an improvement to K Means clustering which is sensitive to outliers.

Machine Learning (ML)

Linear vs Logistic Regression

We have explored the differences between Linear and Logistic regression in depth. We looking into the applications of Linear and Logistic regression along with a basic background.

Machine Learning (ML)

Text classification using K Nearest Neighbors (KNN)

In this article, we will demonstrate how we can use K-Nearest Neighbors (KNN) algorithm for classifying input text into different categories. We used 20 news groups for a demo.

Machine Learning (ML)

PageRank

PageRank is an algorithm to assign weights to nodes on a graph based on the graph structure and is largely used in Google Search Engine being developed by Larry Page

Machine Learning (ML)

Language Identification Techniques

In this article, we will understand the different techniques for language identification which involves two steps namely language modelling and classification

Machine Learning (ML)

Text classification using Naive Bayes classifier

In this article, we have explored how we can classify text into different categories using Naive Bayes classifier. We have used the News20 dataset and developed the demo in Python.

Machine Learning (ML)

Gradient Boosting

Gradient Boosting is a machine learning algorithms used to predict variable (dependent variable). It is used in regression and classification problem.

Machine Learning (ML)

Grid Search

Grid Search is a machine learning tool which is used to tune the hyperparametres of various machine learning algorithms like decision tree and SVM

Machine Learning (ML)

LexRank method for Text Summarization

LexRank method for text summarization is another child method to PageRank method similar to TextRank. It uses a graph based approach for text summarization

Machine Learning (ML)

Applications of Recurrent Neural Networks (RNNs)

In this article, we explored the different applications of RNNs like generating image descriptions, Music composition, Machine translation and more.

Machine Learning (ML)

Graph based approach for Text summarization (Reduction)

In this article we will understand Graph based approach for text summarization (also known as Graph Reduction). It uses techniques to reducing graph size such as predicate-argument mapping and normalization.

Machine Learning (ML)

Image Captioning using Keras (in Python)

Image Captioning is the process of generating a textual description of an image based on the objects and actions in it. We have build a model using Keras library (Python) and trained it to make predictions.

Machine Learning (ML)

Major ideas in Creating Realistic Talking Head Models from Photo

The only issue in creating artificial videos is that they require a large amount of training data, however, we might want it to learn from only a few image views of a person. This is the concern of this paper.

Machine Learning (ML)

AVX512 VNNI: This instruction boosts ML performance by 2X

AVX512 Vector Neural Network Instructions (AVX512 VNNI) is an x86 extension Instruction set and is a part of the AVX-512 ISA. It is designed to accelerate convolutional neural network for INT8 inference

Machine Learning (ML)

Polynomial regression using scikit-learn

In this article, we have implemented polynomial regression in python using scikit-learn and created a real demo and get insights from the results.

Machine Learning (ML)

Learn to use TPOT: An AutoML Tool

TPOT stands for Tree Base Pipeline Optimization Tool. It is used to solve or give a idea on machine learning problems. It helps us to explore some of pipeline confiuration that we did not consider earlier for our model.

Machine Learning (ML)

Exploratory Data Analysis using Python

In this article, we will see what Exploratory Data Analysis (EDA) is, what are the different steps in it, and further how to implement it using Python

Machine Learning (ML)

Edmundson Heuristic Method for text summarization

Edmundson Heuristic Method proposes the use of a subjectively weighted combination of features as opposed to traditionally used feature weights generated using a corpus

Machine Learning (ML)

Applying Naive Bayes classifier on TF-IDF Vectorized Matrix

We will use Naive Bayes classifier on IF-IDF vectorized matrix for text classification task. We use the ImDb Movies Reviews Dataset for this.

Machine Learning (ML)

Hopfield Network

Hopfield Network is a recurrent neural network with bipolar threshold neurons. It consists of a set of interconnected neurons that update their activation values asynchronously.

Machine Learning (ML)

Luhn’s Heuristic Method for text summarization

The idea of Luhn’s Heuristic Method for text summarization is that any sentence with maximum occurrences of the highest frequency words(Stopwords) and least occurrences are not important to the meaning of the document