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Artificial Neural Network, Its inspiration and the Working Mechanism
Artificial Neural Network is made up of layers and layers of connected input units and output units called neurons. Let's get to know it.
Keerthana 14 Apr, 2021
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Applications of Artificial Intelligence and Data Science
Data Science Applications are overtaking the modern era and transforming it into the most exciting fields to look forward to in the future.
pavan 06 Feb, 2024
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A Gentle Introduction to PyTorch Library for Deep Learning
This article covers basic knowledge and working of PyTorch library required to get started with Deep Learning. Follow along with the tutorial
Narasimha 12 Apr, 2021
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Introduction to Autonomous Navigation – LIDAR, Sensor Fusion, Kalman Filter
This article is an introduction to autonomous navigation. We will understand concepts such as LIDAR, SENSOR FUSION, KALMAN FILTER
Srinivas Rahul 11 Apr, 2021
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How did the machine read nutritional facts using Optical Character Recognition?
In this article, we show you how it was possible to read nutritional tables with Optical Character Recognition(OCR), and Tesseract
Guest Blog 09 Apr, 2021
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Softmax Activation Function for Neural Network
Explore the SoftMax activation function and its application in multiclass classification problems. Enhance your understanding of neural network architectures.
Shipra Saxena 01 May, 2025
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Getting started with Kaggle using Facial Detection
In this article, we will guide you to get started with Kaggle using the OpenCV library for facial detection problems in python.
Anu 06 Apr, 2021
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Is Gradient Descent sufficient for Neural Network?
when it comes to building the Deep Learning models, the Gradient Descent has some major challenges. Let's explore them in this article
Neha 01 Apr, 2021
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Introduction to Image Segmentation for Data Science
Image segmentation is the task of partitioning an image based on the objects present. Lets understand image segmentation for data science
Shipra Saxena 30 Mar, 2021
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Introduction to The Architecture of Alexnet
AlexNet architecture: 8 layers, 62.3M parameters, ReLU, dropout, convolution, and deep learning advancements for image recognition.
Shipra Saxena 01 May, 2025
