Tag: artificial neural networks

Hybridizing signatures with artificial intelligence is making a significant difference in our ability to detect cyberattacks, including ransomware.

This article was published as a part of the Data Science Blogathon. Source-Datafloc Introduction Artificial Neural Networks (ANN) have paved a new path to the emerging AI industry since decades it has been introduced. With no doubt in its massive performance and architectures proposed over the decades, traditional machine-learning algorithms are on the verge of extinction […]

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This article was published as a part of the Data Science Blogathon. Table of Contents Gradient Descent Importance of Non-Linearity/Activation Functions Activation Functions (Sigmoid, Tanh, ReLU, Leaky ReLU, ELU, Softmax) and their implementation Problems Associated with Activation Functions (Vanishing Gradient and Exploding Gradient) Endnotes Gradient Descent The work of the gradient descent algorithm is to update […]

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This article was published as a part of the Data Science Blogathon. Table of Contents What is Neural Network Types of Neural Networks Types of Learnings How does a Neural Network work Endnotes What are Neural Networks? Neural networks are used to mimic the basic functioning of the human brain and are inspired by how the […]

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This article was published as a part of the Data Science Blogathon. To understand Convolutional Neural networks, we first need to know What is Deep Learning? Deep Learning is an emerging field of Machine learning; that is, it is a subset of Machine Learning where learning happens from past examples or experiences with the help of […]

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This article was published as a part of the Data Science Blogathon. Introduction If you’re a software developer who’s interested in learning how neural networks function, you’ve come to the perfect spot. We will assist novices to understand what neural networks are, what a neural network model is, and how to expand their knowledge to other […]

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This article was published as a part of the Data Science Blogathon. Introduction Sentiment Analysis is an NLP application that identifies a text corpus’s emotional or sentimental tone or opinion. Usually, emotions or attitudes towards a topic can be positive, negative, or neutral. This makes sentiment analysis a text classification task. Examples of positive, negative, and […]

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Data Science models come with different flavors and techniques — luckily, most advanced models are based on a couple of fundamentals. Which models should you learn when you want to begin a career as Data Scientist? This post brings you 6 models that are widely used in the industry, either in standalone form or as a building block for other advanced techniques.

This article was published as a part of the Data Science Blogathon. Introduction to Artificial Neural Network Artificial neural network(ANN) or Neural Network(NN)...

GlossaryCentral carbon metabolism (CCM)as the main source of energy, CCM oxidizes carbon through glycolysis, the pentose phosphate pathway, and the tricarboxylic acid cycle.Chassisa...

Artificial intelligence in manufacturing is a trendy term. When describing AI-based defect detection solutions, it’s often about visual inspection technology based on deep learning...

LoRaWAN Strengths and WeaknessesThere is now a wide range of different wireless technologies for sensor data transmission and condition monitoring in industrial IoT...