Introduction Autoencoders are unstructured learning models that utilize the power of neural networks to perform the task of representation learning. In the context of machine learning, representation learning means embedding the components and features of original data in some low-dimensional structure for better understanding, visualizing, and extraction of meaningful information. …

Problem Statement In this article, we will try to solve a problem where we need to find whether the two addresses provided indicated the same location or different. Naturally, the simple way to do this is to perform an exact string match between the two addresses, but the problem arises when the…

The part focuses on the actual machine learning model creation, the first part here focussed on data understanding, data preprocessing, and analysis. Problem Description : We aim to create a product category API that utilizes machine learning and deep learning to predict the possible categories/classes for any provided product name and its descriptions…

Problem Description : We aim to create a product category API that utilizes machine learning and deep learning to predict the possible categories/classes for any provided product name and its descriptions. …

Target Audience This article is for students and professionals who have an interest in Machine Learning and are starting their journey in this field. You can consider this article as the gist of machine learning which will help you create basic foundations and clarity regarding this field. What is machine learning?

In this particular article, we will consider the problem of receipt digitization i.e extracting necessary and important information in form of labels from hardcopy receipts such as medical invoices, tickets, etc. These kinds of models can be highly useful in real life and help users, to better understand data as…

Introduction to Computer Vision pipeline We will be creating a vehicle detection and classification pipeline using machine learning techniques. We will be detecting the number of cars per frame and classify them into two classes SUV and sedan, all in real-time. This article has a few prerequisites such as the basics of machine learning…

Previous post: Transitioning from Supervised Learning systems to Multi-Agent Reinforcement learning for financial… Previous post :prakhargurawa.medium.com Application of Reinforcement Learning on our financial systems Describing existing machine learning framework for our financial system The current model is based on supervised-based learning where the typical workflow can be represented as data collection, model training, trading policy, backtesting, and live testing. The live data is bought from sources like Quandl and AlphaVintage which is then split into training, testing, and validation which is…

Previous post : Transitioning from Supervised Learning systems to Multi-Agent Reinforcement learning for financial… Abstractprakhargurawa.medium.com Understanding agents,multi-agents, and cooperation among multi-agents Agents Russell and Norvig [1] state agents as anything that can perceive the environment through sensors and act upon it using its actuators. Macal [2] describes agents as objects with characteristics such as identifiable — as a discrete individual that govern decision-making capacity, located — settled in an…