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
Deep neural networks are at the heart of artificial intelligence, ranging from pattern recognition to large language and reasoning models like ChatGPT. The principle: during a training phase, the parameters of the network's
Blog Topics Advertise Join Newsletter Understanding How Neural Networks Think A couple of years ago, Google published one of the most seminal papers in machine learning interpretability. By Jesus Rodriguez , Intotheblock on July 16, 2020 in Google , Interpretability , Machine Learning --> comments Source: https://distill.pub/2018/building-blocks/ I recently started a new newsletter focus on AI education. TheSequence is a no-BS (meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read
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 Artificial Intelligence Neural Networks from a Bayesian Perspective Learn how to estimate model uncertainty in neural networks Yoel Zeldes Aug 6, 2018 7 min read Share Understanding what a model doesn’t know is important both from the practitioner’s perspective and for the end users of many different machine learning applications. In
How neural geometry will unlock understanding and control of AI
How neural geometry will unlock understanding and control of AI
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
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
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