Machine Learning (ML)

Simplifying "Intriguing properties of neural networks"

In this article, we have explored the paper "Intriguing properties of neural networks" by Christian Szegedy in depth as it is an influential paper which introduces two key properties that define Neural Networks.

Understanding the VGG19 Architecture

VGG19 is a variant of VGG model which in short consists of 19 layers (16 convolution layers, 3 Fully connected layer, 5 MaxPool layers and 1 SoftMax layer). There are other variants of VGG like VGG11, VGG16 and others. VGG19 has 19.6 billion FLOPs.

Machine Learning (ML)

Gaussian Naive Bayes

Gaussian Naive Bayes is a variant of Naive Bayes that follows Gaussian normal distribution and supports continuous data. We have explored the idea behind Gaussian Naive Bayes along with an example.

Machine Learning (ML)

Optical Character Recognition (OCR)

Optical Character Recognition or OCR is the technology that is used to convert characters or text that is either handwritten or printed in the form of paper into machine encoded text so that it can be saved digitally.

Machine Learning (ML)

Simplifying "Gaussian LDA for Topic Models with Word Embeddings"

In this article, we have explained the research paper titled "Gaussian LDA for Topic Models with Word Embeddings" by Rajarshi Das, Manzil Zaheer, Chris Dyer (associated with Carnegie Mellon University) in a easy fashion for beginners to get deep understanding of this paper

Machine Learning (ML)

Pachinko Allocation Model (PAM)

Pachinko Allocation Model (PAM) is a topic modeling technique which is an improvement over the shortcomings of Latent Dirichlet Allocation. In this article, we have explained it in detail.

Machine Learning (ML)

BERT for text summarization

BERT (Bidirectional tranformer) is a transformer used to overcome the limitations of RNN and other neural networks as Long term dependencies. We have explored in depth how to perform text summarization using BERT.

Machine Learning (ML)

Tensorflow.js: Machine Learning through JavaScript

Tensorflow.js is an open-source library with which we can implement machine learning in the browser with the help of JavaScript. It is powered by WebGL and provides a high-level layers API for defining models, and a low-level API for linear algebra and automatic differentiation.

Machine Learning (ML)

Convolutional Neural Network (CNN) questions

In this article, we have presented the most insightful and must attempt questions on Convolutional Neural Network (CNN) along with detailed answers so that you can understand CNN in depth.

Machine Learning (ML)

Batch Normalization

Batch normalization is a technique used to increase the stability of a neural network. It helps our neural network to work with better speed and provide more efficient results.

Machine Learning (ML)

Guide to "An embarrassingly simple approach to Zero-Shot Learning"

In this article, we have discussed about "An embarrassingly simple approach to Zero-Shot learning" and dived into details how to apply this approach that in a single line of code outperforms the state-of-art models.

Machine Learning (ML)

Types of RNN (Recurrent Neural Network)

In this article, we shall dive into Recurrent Neural Networks types after getting you briefly introduced to RNNs. In short, the different types of RNN are one to one, one to many, many to many and many to one.

Machine Learning (ML)

Understand different types of Boosting Algorithms

In this article we will dive deep into understanding Boosting and then we are going to see rapidly some derived algorithms like AdaBoost and LightGBM.

Machine Learning (ML)

Topic Modeling using Non Negative Matrix Factorization (NMF)

Non-Negative Matrix Factorization is a statistical method to reduce the dimension of the input corpora. It uses factor analysis method to provide comparatively less weightage to the words with less coherence.

Machine Learning (ML)

Sentiment Analysis Techniques

Sentiment Analysis is the application of analyzing a text data and predict the emotion associated with it. This is a challenging Natural Language Processing problem and there are several established approaches which we will go through.

Machine Learning (ML)

Topic Modeling using Latent Semantic Analysis

In this article, we have explored the functioning and working of Latent Semantic Analysis with respect to topic modeling in DEPTH along with mathematics behind the method.

Machine Learning (ML)

Residual Neural Network (ResNet)

Residual neural networks or commonly known as ResNets are the type of neural network that applies identity mapping to solve the vanishing gradient problem and perform better than RNN and CNN.

Machine Learning (ML)

Text Summarization using RNN

Encoder Decoder RNN (Recurrent neural network) model is used in order to overcome all the limits faced by the NLP for text summarization such as getting a short and accurate summary.

Machine Learning (ML)

Apriori Associative Learning Algorithm

Apriori is a associative learning algorithm which is generally used in data mining. It follows the principle that people who bought this will also buy this.

Machine Learning (ML)

Latent Dirichlet Allocation (LDA)

Latent Dirichlet Allocation (LDA) is used as a topic modelling technique that is it can classify text in a document to a particular topic. It uses Dirichlet distribution to find topics for each document model

Machine Learning (ML)

Topic Modelling Techniques in NLP

Topic modelling is an algorithm for extracting the topic or topics for a collection of documents. We explored different techniques like LDA, NMF, LSA, PLDA and PAM.

Machine Learning (ML)

Zero shot learning: Approach that can change Machine Learning

In zero-shot learning, the machine is capable of describing what class an unlabeled sample belongs to when it does not fall into the category of any of the trained categories.

Machine Learning (ML)

Implement Document Clustering using K Means in Python

In this article, we discuss the implementation of concepts like TF IDF, document similarity and K Means and created a demo of document clustering in Python

Machine Learning (ML)

TextRank for Text Summarization

TextRank is a text summarization technique which is used in Natural Language Processing to generate Document Summaries. It uses an extractive approach and is an unsupervised graph-based text summarization technique based on PageRank.

Software Engineering

Create a Web App using Flask to present a Machine Learning model

We will see how to deploy a Machine Learning model by building a simple Web Application using Flask. It will run the Machine Learning model in the server as inference.