Product pricing plays a pivotal role at various stages of a product lifecycle and has a direct impact on a brand’s bottom line. In this blog post, we shall use the explore-exploit strategy for determining the optimal price for a SINGLE product.
Neural Machine Translation is the use of Deep Neural Networks for translating a text from one language (source language) to its counterpart in other language (target language).
KL divergence is used to compare probability distribution functions. This article focuses on deriving a closed form solution for KL divergence using in Variational Autoencoders.
The goal of this article is to understand and derive the ELBO (Evidence Lower Bound) cost function used in training Variational Autoencoder. The article is designed with an assumption that the readers possess basic understanding of Generative Modelling and Variational Autoencoders.
In many real-world data applications, often we encounter scenarios where each data point may belong to multiple classes. A multilabel classifier is trained to predict the K most likely classes among N possible classes. The article focuses on solving multi-label text classification problems using the Hierarchical Attention Network.
The article covers step-by-step proof for proving the convexity of a mean squared error loss function. The Ability to test convexity for different loss functions can come in handy especially with more and more exotic loss functions being proposed every day.
The convexity property of a function unlocks a crucial advantage where the local minima of a convex function is also a global minima. This ensures that a model can be trained where the loss function is minimized to its globally minimum value. In this blog post, we shall work through the concepts needed to prove the convexity of a function.
There is a huge surge in number of Machine Learning based products which are being actively researched and developed across the globe. One of the crucial factors in the delivery of an ML product is the ability to expose the trained model/predictions to the world. In this article, I will provide a step-by-step guide for developing a web app using Flask.
With the advent of GANs and its variations, Generative Modelling has picked up pace in deep learning research. This article is aimed at providing a gentle introduction to Generative modeling by leveraging Multivariate Normal Distribution.
As a part of "Getting Acquainted with Deep Learning Frameworks" series, in the article we shall explore Pytorch Library. Pytorch is a deep learning library developed by Facebook Researchers. The focus of this article will be to highlight the steps involved in training a multiclass classifier using Pytorch.
Getting started with deep learning frameworks often involves a steep learning curve. This article is aimed at providing a gentle introduction to building DNN models with Keras which can be scaled and customized as per dataset. The focus will be on understanding the syntax and good practices involved in building a complex DNN model rather than achieving accuracy.
Extracting Key Phrases from Textual data is a problem faced across domains. In this Article, we shall explore an approach which leverages Google's pagerank algorithm to solve the problem. Basic knowledge of Linear Algebra, Markov Chains and Text Parsing would help in comprehending the content.
With all the buzz around Indian Premier League (IPL), FIFA World Cup, Cricket World Cup-2019, fantasy sports websites like Dream11 are gaining traction. Fanatsy Sports Portal allows sports fans like me to be a part of it. Having said that, it is very difficult to keep track of all the players and their performance across the sports. In this Tutorial, I will try to automate the fantasy selection process so that the probability of winning a fantasy league is maximized.
Image classification using deep learning and its applications has been proving its worth across business verticals.However, building an image centric AI product is often marred by Unavailability of large amounts of data, poor quality of images, etc. Often the metadata / textual data associated with these images are ignored. In this article, we shall try and build a DL model that can leverage image as well as textual data.
Logistic Regression is one the most basic algorithm on ML. With the likes of sklearn providing an off the shelf implementation of Linear Regression, it is very difficult to gain an insight on what really happens under the hood. This tutorial is aimed at implementing Logistic Regression from scratch in python using Numpy.
Linear Regression is one the most basic algorithm on ML. With the likes of sklearn providing an off the shelf implementation of Linear Regression, it is very difficult to gain an insight on what really happens under the hood. This tutorial is aimed at implementing Linear Regression from scratch in python using Numpy.