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neural networks

What is Artificial Intelligence in 2024? Types, Trends, and Future of it?

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What is Artificial Intelligence? Artificial Intelligence is defined as the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. AI is also defined as, A layman with a fleeting understanding of technology would link it to robots. They’d say Artificial Intelligence is a terminator like-figure that can act […]

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Energy-Efficient AI and Transformation of Sports in 2020 – Weekly Guide

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The year 2020 has been dominated by the COVID-19 pandemic and the transformations that have come with it. Whether it be the social transformations, the new working normal, and many other transformations. In this week’s AI guide, we will see how AI transformed sports in 2020. In other news, researchers from CWI have made a

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brain computer interface

A Beginner’s Guide to Brain-Computer Interface

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Brain Computer Interface technology, also commonly used as BCI technology is a direct interface between humans and systems, bypassing the need for any external device and muscle intervention to the need of issuing any commands and codes. Contributed by: Devanshi During my latest business visit to Singapore, I happened to travel by MRT – I

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gaussian dropout

Understanding Gaussian Dropout

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Overfitting is a serious problem in neural networks. To understand Gaussian Dropout, we must first understand what overfitting means. Contributed by: Ribha Sharma What is overfitting?  When a model is good at classifying or predicting data in the train set but is not so good at classifying data on a test data set. It can be

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Variational autoencoder

Understanding Variational autoencoder

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Contributed by: Tejas Variational autoencoder is one of the most talked-about technologies in Machine Learning. Why? What does it do? Let’s start with understanding encoders and decoders in our daily life. For example, the radio or television signals that are relayed from the station are encoded, and once our device receives it, it decodes and

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activation functions

Activation Functions in Neural Networks Explained

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Contributed by: Sreekanth LinkedIn Profile: https://www.linkedin.com/in/sreekanth-tadakaluru-3301649b/ Introduction Activation functions are mathematical equations that determine the output of a neural network model. Activation functions also have a major effect on the neural network’s ability to converge and the convergence speed, or in some cases, activation functions might prevent neural networks from converging in the first place.

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Neural network

What is Recurrent Neural Network | Introduction of Recurrent Neural Network

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Before starting with neural networks, let’s have a look at the basics of neural networks. Neural networks are considered as the most powerful and widely used algorithms. It is the subfield of machine learning which is called deep learning. For the beginners who just start their journey with neural networks, for them maybe neural networks

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