Hi for the assignment, Logistic_Regression_with_a_Neural_Network_mindset i am getting an error at exercise 8 - ValueError Traceback (most recent call last) in 1 from public_tests imp

Neural networks are interpreted as dynamical systems on a latent manifold, enabling the analysis of generalization, prior knowledge extraction, and out-of-distribution

Music exists in almost every society, has universal acoustic features, and is processed by distinct neural circuits in humans even with no experience of musical training. However, it remains unclear how these innate characteristics emerge and what functions they serve. Here, using an artificial deep neural network that models the auditory information processing of the brain, we show that units tuned to music can spontaneously emerge by learning natural sound detection, even without learning music. The music

The enormous inference cost of deep neural networks can be scaled down by network compression. Pruning is one of the predominant approaches used for deep network compression. However, existing pruning techniques have one or more of the following limitations: 1) Additional energy cost on top of the compute heavy training stage due to pruning and fine-tuning stages, 2) Layer-wise pruning based on the statistics of a particular, ignoring the effect of error propagation in the network, 3) Lack of an efficient e

A convolutional neural network (CNN) is a powerful tool for processing grid-like data, particularly images. Its design e

# Botober 2025: Terrible recipes from a tiny neural net After seeing generated text evolve from the days of tiny neural networks to today's ChatGPT-style large language models, I have to conclude: there's something special about the tiny guys. Maybe it's the way the tiny neural networks string together text letter by letter just based on what you've given it, rather than drawing from prior internet training. It's not secretly drawing on some dark corner of the internet, it's just mashing together statisti

Artificial Neural Networks (ANNs) have revolutionized machine learning, powering applications from image recognition to natural language processing. However, raw neural network outputs—often called "logits"—are not inherently interpretable as probabilities. For tasks like classification, fraud detection, or medical diagnosis, we need outputs that represent the *likelihood* of a sample belonging to a class. Converting logits to probabilities transforms abstract numerical scores into actionable insights

Nov 4, 2025 1 min read Papers New paper on dynamic connectivity in adaptive networks A new paper by Christian Bick and Erik A. Martens, Roskilde University, Denmark, explores how complex systems like brains and communication networks change when connections adapt over time. The work reveals how adaptation constraints can reduce complexity stemming from network adaptation. This allows, for example, to understand transition to collective behaviours such as synchronisation in adaptive networks. Many systems th

How to Implement Transfer Learning Effectively Transfer learning allows models to leverage pre-trained networks for new tasks

(Phys.org)—A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published