A chronicle of findings and observations I've made while experimenting with learning logic and neural networks. Topics include developing a new activation function, estimating Boolean and Kolmogorov complexity, and reverse-engineering a neural network's solution

Blog Topics Advertise Join Newsletter TensorFlow: Building Feed-Forward Neural Networks Step-by-Step This article will take you through all steps required to build a simple feed-forward neural network in TensorFlow by explaining each step in details. By Ahmed Gad , KDnuggets Contributor on October 23, 2017 in Deep Learning , Neural Networks , TensorFlow --> In this article, two basic feed-forward neural networks (FFNNs) will be created using TensorFlow deep learning library in Python. The reader should have

Last year I worked for a bit on a fun research project that ended up published as an arXiv “pre-print” / technical report and here comes a few paragraph “normal language” description of this work. Neural Networks are taking over image processing. If you only read conference papers and watch marketing materials, it’s easy to

# Neural networks Are We Thinking Correctly About AI Intelligence? ### Are We Thinking Correctly About AI Intelligence? By Steven Strogatz +1 author Janna Levin August 20, 2026 Computer scientist Melanie Mitchell discusses why artificial intelligence doesn’t “think” or “reason” like humans, and how we can create better methods for measuring machine cognition. Why Do Humanoid Robots Still Struggle With the Small Stuff? ### Why Do Humanoid Robots Still Struggle With the Small Stuff? March 13, 2026 T

7. Convolutional Neural Networks 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 Scratch 3.5. Concise Implementation of Line

How neural geometry will unlock understanding and control of AI

Feedforward neural networks 1. What is a feedforward neural network? A feedforward neural network is a biologically inspired classification algorithm. It consist of a (possibly large) number of simple neuron-like processing units, organized in layers. Every unit in a layer is connected with all the units in the previous layer. These connections are not all equal: each connection may have a different strength or weight. The weights on these connections encode the knowledge of a network. Often the units in a

Summary of the 2014 article "Sequence to Sequence Learning with Neural Networks" by Sutskever et al

Neural Networks and Deep Learning Course: Part 1

Aller au contenu principal Complex Networks We are interested in all aspects of real world networks and their models, from internet measurements to random graphs, from social network analysis to spreading phenomena, and from graph algorithms to biological networks. Menu Publié le décembre 31, 2025janvier 21, 2026 par admin Multi-relational Community Detection in Social Platforms Using Graph Neural Networks Nouamane Arhachoui, Vincent Gauthier, Anastasios Giovanidis, Lionel Tabourier In France