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Posts about neural written by Paul van der Laken

# Detecting adversarial examples in deep neural networks Fabio Carrara , Fabrizio Falchi , Roberto Caldelli , Giuseppe Amato , Roberta Fumarola, Rudy Becarelli ## Abstract Deep learning has recently become state-of-the-art in many computer vision applications and in image classification in particular. It is now a mature technology that can be used in several real-life tasks. However, it is possible to create adversarial examples, containing changes unnoticeable to humans, which cause an incorrect classif

Backpropagation for fully-connected neural networks

7. Convolutional Neural Networks navigate_next 7.2. Convolutions for Images search Quick search code Show Source Table Of Contents 1. Introduction 2. Preliminaries 2.1. Data Manipulation 2.2. Data Preprocessing 2.3. Linear Algebra 2.4. Calculus 2.5. Automatic Differentiation 2.6. Probability and Statistics 2.7. Documentation 3. Linear Neural Networks for Regression 3.1. Linear Regression 3.2. Object-Oriented Design for Implementation 3.3. Synthetic Regression Data 3.4. Linear Regression Implementation from

Despite the unresolved 'dying ReLU problem', the classical ReLU activation function (AF) has been extensively applied in Deep Neural Networks (DNN), in particular Convolutional Neural Networks (CNN), for image classification. The common gradient issues of ReLU pose challenges in applications on academy and industry sectors. Recent approaches for improvements are in a similar direction by just proposing variations of the AF, such as Leaky ReLU (LReLU), while maintaining the solution within the same unresolve

Blog Topics Advertise Join Newsletter Build an Artificial Neural Network From Scratch: Part 1 This article focused on building an Artificial Neural Network using the Numpy Python library. By Nagesh Singh Chauhan , KDnuggets on November 1, 2019 in Neural Networks , numpy , Python --> comments In my previous article Introduction to Artificial Neural Networks(ANN) , we learned about various concepts related to ANN so I would recommend going through it before moving forward because here I’ll be focusing on

# Mixture Density Networks with TensorFlow ## Mixture Density Networks with TensorFlow November 24, 2015 Update: PyTorch Implementation of the same notebook available here . A short while ago Google open-sourced TensorFlow , a library designed to allow easy computations on graphs. The main applications are targeted for deep learning, as neural networks are represented as graphs . I’ve spent a few days reading their APIs and tutorials and I like what I see. Although other libraries offer similar features

Abstract page for arXiv paper 2012.07564: ALReLU: A different approach on Leaky ReLU activation function to improve Neural Networks Performance

Skip to content TheLinuxCode Software Menu Toggle Distros Menu Toggle SysAdmin Menu Toggle Residential Proxies Residential Proxies TheLinuxCode Main Menu Menu Convolutional Neural Networks in R: A Practical Guide from Notebook Baseline to Production Leave a Comment / By Linux Code / February 13, 2026 If you have ever shipped a model that looked perfect in a notebook but failed on real images, you already know the hard truth: image ML is mostly about disciplined engineering, not clever layer stacking. I lear