Analytics Insight

Best Deep Learning Books to Read in 2026

Overview: Master deep learning with these 10 essential books blending math, code, and real-world AI applications for lasting ...
Deep Learning with Yacine on MSN

RMSProp optimization from scratch in Python

Understand and implement the RMSProp optimization algorithm in Python. Essential for training deep neural networks ...
Deep Learning with Yacine on MSN

Deep neural network from scratch in Python – fully connected feedforward tutorial

Learn how to build a fully connected, feedforward deep neural network from scratch in Python! This tutorial covers the theory, forward propagation, backpropagation, and coding step by step for a hands ...
IEEE

Understanding Error Propagation in Deep-Learning Neural Networks’ Accelerators and Applications

Abstract: Editor’s notes: This article reviews some of the first experiments on deep neural networks to analyze the propagation of soft errors from hardware to the ...
Beebom

What is CNN in Deep Learning? The AI Behind Computer Vision

CNN in deep learning is a special type of neural network that can understand images and visual information. It works just like human vision: first it detects edges, lines and then recognizes faces and ...
blockchain

Celebrating Geoffrey Hinton: AI Pioneer’s Impact on Deep Learning and Neural Networks

According to Jeff Dean on Twitter, Geoffrey Hinton, often referred to as the 'Godfather of AI,' celebrates his birthday today. Hinton's pioneering research in neural networks and deep learning has ...
techxplore

Overparameterized neural networks: Feature learning precedes overfitting, research finds

Modern neural networks, with billions of parameters, are so overparameterized that they can "overfit" even random, structureless data. Yet when trained on datasets with structure, they learn the ...
EurekAlert!

Dynamic network plasticity and sample efficiency in biological neural cultures: A comparative study with deep reinforcement learning

(A) Schematic illustration of the DishBrain feedback loop, the simulated game environment, and electrode configurations. (B) A schematic illustration of the overall network construction framework. The ...
Frontiers

Review of deep learning models with Spiking Neural Networks for modeling and analysis of multimodal neuroimaging data

Medical imaging has become an essential tool for identifying and treating neurological conditions. Traditional deep learning (DL) models have made tremendous advances in neuroimaging analysis; however ...
GitHub

What are neural networks?

What is a neural network? A neural network, also known as an artificial neural network, is a type of machine learning that works similarly to how the human brain processes information. Instead of ...
Frontiers

Effective methods and framework for energy-based local learning of deep neural networks

From a neuroscience perspective, artificial neural networks are regarded as abstract models of biological neurons, yet they rely on biologically implausible backpropagation for training. Energy-based ...