News Neural Basics Neural Basics Neural Basics # Neural Basics ## Meta’s AI reads typed sentences from the brain, no surgery required - Ana Maria Constantin - Jul 1, 2026 ## What is the difference between artificial neural networks and biological brains? - Ben Dickson - Jul 16, 2020 ## What is the AI brain drain? - Ben Dickson - Jun 30, 2020 ## What is generative adversarial network (GAN) — and how it makes computers creative - Ben Dickson - Jun 29, 2020 ## Everything you need to know about recurr
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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 Deep Learning Illustrated, Part 3: Convolutional Neural Networks An illustrated and intuitive guide on the inner workings of a CNN Shreya Rao May 11, 2024 16 min read Share Welcome to Part 3 of our illustrated journey through Deep Learning. If you’ve missed the previous articles, definitely go back to read them
View all by clementneoclementneo Follow clementneoFollowFollowing clementneoFollowing Add To CollectionCollection Comments Submission to The Interpretability Hackathon Related gamesRelated Algorithmic Explanation: A method for measuring interpretations of neural networks A downloadable game Download How do you make good explanations for what a neural network does? We provide a framework for analysing explanations of the behaviour of neural networks by looking at the hypothesis of how they would act on a set
mailitics Accelerating Hamiltonian Monte Carlo for Bayesian Inference in Neural Networks and Neural Operators Accelerating Hamiltonian Monte Carlo for Bayesian Inference in Neural Networks and Neural Operators arXiv:2507.14652v1 Announce Type: new Abstract: Hamiltonian Monte Carlo (HMC) is a powerful and accurate method to sample from the posterior distribution in Bayesian inference. However, HMC techniques are computationally demanding for Bayesian neural networks due to the high dimensionality of the netw
In this video, we explain the concept of artificial neural networks and show how to create one (specifically, a multilayer perceptron or MLP) in code with Keras
NeurIPS Proceedings Search Practical Variational Inference for Neural Networks Alex Graves Advances in Neural Information Processing Systems 24 (NIPS 2011) Abstract Variational methods have been previously explored as a tractable approximation to Bayesian inference for neural networks. However the approaches proposed so far have only been applicable to a few simple network architectures. This paper introduces an easy-to-implement stochastic variational method (or equivalently, minimum description length los
Although deep neural networks have been immensely successful, there is no comprehensive theoretical understanding of how they work or are structured. As a result, deep networks are often seen as black boxes with unclear interpretations and reliability. Understanding the performance of deep neural networks is one of the greatest scientific challenges. This work aims to apply principles and techniques from information theory to deep learning models to increase our theoretical understanding and design better a
Computing systems inspired by biological neural networks, modeling complex patterns through interconnected artificial neurons
# The Keras Blog Keras is a Deep Learning library for Python, that is simple, modular, and extensible. # How convolutional neural networks see the world Sat 30 January 2016 By Francois Chollet In Demo . Note: this post was originally written in January 2016. It is now very outdated. Please see this example of how to visualize convnet filters for an up-to-date alternative, or check out chapter 9 of my book "Deep Learning with Python (2nd edition)". ## An exploration of convnet filters with Keras In th