Skip to content Publish AI, ML & data-science insights to a global community of data professionals. Sign in Submit an Article Toggle Mobile Navigation Toggle Search Search Deep Learning Understanding Convolutional Neural Networks (CNNs) Through Excel Exploring the frontier between human and machine rules angela shi Nov 17, 2025 14 min read Share Deep learning is often seen as a black box. We know that it learns from data, but the question is how it truly learns. In this article, we will build a tiny Convolu

Neural networks today do everything from cameras to translations. A professor of computer science provides a basic explanation of how neural networks work

Ph. D. Thesis 2. Theory � Fundamentals of the Multivariate Data Analysis 2.7. Neural Networks � Universal Calibration Tools 2.7.1. Principles of Neural Networks 2.1. Overview of the Multivariate Quantitative Data Analysis 2.2. Experimental Design 2.3. Data Preprocessing 2.4. Data Splitting and Validation 2.5. Calibration of Linear Relationships 2.6. Calibration of Nonlinear Relationships 2.7. Neural Networks � Universal Calibration Tools 2.7.1. Principles of Neural Networks 2.7.2. Topology of Neura

Learn what an LSTM neural network is, how it works, the benefits and limitations compared to other kinds of neural networks, common uses, and specific industry applications

Downloads Cloud Login Innovation & Technology Products Consulting Why Franz Inc. LLMs, Prompt Engineering, and Neuro-Symbolic AI Entity – Event Knowledge Graphs Taxonomy and Ontology Design Master Data Management Data Integration Solutions Solution Implementation Training Tag Archives: graph neural network Home Posts Tagged "graph neural network" Predictions: Quantum AI, Graph Neural Networks, and Personal Data Pods By Franz Inc. December 15, 2021 Fuse Graph Neural Networks with Semantic Reasoning to

What is dropout in deep neural networks Dropout refers to data or noise thats intentionally dropped from a neural network to improve processing and time

In recent years, the best-performing artificial-intelligence systems—in areas such as autonomous driving, speech recognition, computer vision, and automatic translation—have come courtesy of software systems known as neural

We applied a mechanistic interpretability approach to neural networks trained on a large yeast genotype–phenotype dataset. This allowed us to uncover novel evidence of environmentally mediated global epistasis in these data

Learn about Regression Analysis Using Artificial Neural Networks in Deep Learning with Scaler Topics

NeurIPS Proceedings Search Feedback control guides credit assignment in recurrent neural networks Klara Kaleb, Barbara Feulner, Juan A. Gallego, Claudia Clopath Advances in Neural Information Processing Systems 37 (NeurIPS 2024) Main Conference Track Abstract How do brain circuits learn to generate behaviour? While significant strides have been made in understanding learning in artificial neural networks, applying this knowledge to biological networks remains challenging. For instance, while backpropagation