Unlocking the Secret Behind Neural Networks' Symmetry Superpowers Have you ever wondered why neural networks seem to naturally pick up on symmetries in data, even when we don't explicitly tell them to? Researchers have long observed this phenomenon, but the why behind it has remained a mystery. Now

Thomas Shultz, Professor @ McGill University Learning & development Neural networks Memory Evolution Cognitive dissonance Problem solving Decision making Commentaries Blog posts Research highlights Resolving the St. Petersburg paradox Spread of innovation in wild birds Resolving Rogers' paradox Evolution of ethnocentrism Shape of development Connectionist modeling Neural networks Symbolic modeling Causal reasoning Moral reasoning Theory of mind Development of humor LNSC Neural networks With Yoshio Takane an

General-purpose technique sheds light on inner workings of neural nets trained to process language

Backpropagation is so commonplace in neural network training that it is easy to take it for granted. But what is it

Learn what are neural networks, how they work, and why they power modern AI. A clear, jargon-free guide with real-world examples and diagrams

# Do neural networks learn human concepts? This page is a stub. It does not necessarily represent much of what is known on the topic. Our understanding is that the degree to which neural networks learn concepts that are potentially understandable to humans is an open question. ## Details A very incomplete list of sources on the topic: - Acquisition of Chess Knowledge in AlphaZero (McGrath et al, 2021) 1 From the paper: ‘…In this work we provide evidence that human knowledge is acquired by the AlphaZer

Neural networks and GenAI strategy for the future of business. Why leaders must develop solid strategic AI positions for these technologies

Dense Neural Networks (DNNs) are fully-connected architectures powering image, speech, and sequence tasks, optimized with advanced training methods like DSD

OpenAI is exploring mechanistic interpretability to understand how neural networks reason. Our new sparse model approach could make AI systems more transparent and support safer, more reliable behavior

7. Convolutional Neural Networks navigate_next 7.6. Convolutional Neural Networks (LeNet) 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 Implem