Artificial Intelligence

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include: Speech recognition. Learning.

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Visualizing AI startups in drug discovery

As a machine learning researcher in the biology field, I have been keeping an eye on the recently emerging field of AI in drug discovery

Generate stories using RNNs |Pure Mathematics with code|

This article presumes that you are unreasonably fascinated by the mathematical world of deep learning. You want to dive deep into the math of deep learning to know what’s actually going under the hood.

Gender and Geographic Origin Biases

This article took a long time to prepare. Thank you to Professor Miodrag Bolic from the University of Ottawa for reviewing this article and providing valuable feedback.

The 3 Most Important Composite Classification Metrics

Composite classification metrics help you and other decision makers evaluate the quality of a model quickly. They often provide more valuable information than simple metrics such as recall, precision.

Intuition and Implementation of Gradient Boost Part-2

Understanding the Math Intuition and Implementation of Gradient Boost for Classification Problem…! Here we will understand how Gradient Boosting Algorithm works for Classification Problem.

Do Enterprises Need an Operating System (OS) for AI?

The growth of many new open-source tools centered around Kubernetes has spurred the need to string together these tools to form production AI pipelines with glue code." - Rush Tehrani, CEO Onepanel

Getting the Intuition of Graph Neural Networks

Getting the Intuition of Graph Neural Networks. This article would mainly touch on some basic theory and how to translate graphs into features that can be used by neural networks and some other applications of GNNs.

Top 8 Python Tools For App Development One Must Know

In one of the surveys by AIM, 53.3% of data scientists prefer this language as it helps them build specific analytics capabilities.

Stacked Capsule Autoencoders

A look into the future of object detection in images and videos using Unsupervised Learning and a limited amount of training data. During the last few years, Geoffrey Hinton and a team of researchers started working on a revolutionary new type of neural network based on Capsules.

Importance of Sigmoid activation function in the Logistic Regression Model

Today’s discussion: Now we need to maximize the above function, more the value of the function much better is the plane. Since Yi and xi are constants we can not use them to maximize our function, we have to vary w such that it maximizes the value of the function.

Support Vector Machines (SVM) and its Python implementation

Support Vector Machines (SVM) and its Python implementation. The support vector machines algorithm is a supervised machine learning algorithm that can be used for both classification and regression. In this article, we will be discussing certain parameters concerning the support vector machines and try to understand this algorithm in detail.

How ICICI Lombard Leverages AI and Analytics For Automated Processing Of Insurance Claims

In the last decade, insurance providers have had to move away from their traditional core systems towards more flexible, cloud-based applications that have auto-scaling capabilities. This has been done to help consumers have a seamless insurance purchase and a faster and better consumer experience. In this context, the insurance industry is leveraging several technologies, including…

Roadmap to Natural Language Processing (NLP)

An introduction to some of the most common techniques and models used in Natural Language Processing (NLP) Due to the development of Big Data during the last decade. organizations are now faced with analysing large amounts of data coming from a wide variety of sources on a daily basis.

Natural Language Processing with Python, Scikit-Learn

Learn Natural Language Processing in Python With a Project. Natural Language Processing with Python, Scikit-Learn. Natural Language Processing(NLP) refers to developing an application that understands human languages. There are so many use cases for NLPs nowadays. Because people are generating thousands of gigabytes of text data every day through blogs, social media comments, product reviews, news archives, official reports, and many more. Search Engines are the biggest example of NLPs. I don’t think you will find very many people around you who never used search engines.

Top 8 Online Resources To Learn Anaconda In 2020

One of the popular Python platforms for data scientists, Anaconda, contains tools, editors, and more than 500 packages.

How to Implement Convolutional Autoencoder in PyTorch with CUDA

How to Implement Convolutional Autoencoder in PyTorch with CUDA - ConvAE for Image Construction - Deep Convolutional Autoencoder

Deploying an Image Classification Web App with Python

How to deploy your machine learning model using Streamlit and Heroku. Deploying an Image Classification Web App with Python. If you’re using Python, you can use Streamlit library to create a simple machine learning web app in your local machine. To deploy the web app to be accessible to other people, then we can use Heroku or other cloud platforms. In this article, I will show you step-by-step on how to create your own simple web app for image classification using Python, Streamlit, and Heroku.

Netmagic To Create Centre Of Excellence In Association With Google Cloud

Netmagic Solutions, a managed cloud hosting and multi-cloud hybrid IT solution provider, on Tuesday, announced its association with Google Cloud to create a centre of excellence (CoE).

What Can Video Games Teach About Data Science

Studies have suggested that video games and the gaming industry is one of the most engaging ways to develop perceptual, cognitive, behavioural, affective and motivational impacts and outcomes in an individual.

ML Infrastructure Tools for Model Building

Model Building — The Second Stage of ML Workflow. Artificial Intelligence (AI) and Machine Learning (ML) are being adopted by businesses in almost every industry. Many businesses are looking towards ML Infrastructure platforms to propel their movement of leveraging AI in their business. Understanding the various platforms and offerings can be a challenge.