Deep Learning Course Lecture 01: Introduction to Neural Networks Lecture 02: Training Neural Networks Lecture 03: Architecture Lecture 04: Tuning Lecture 05: CNN Section 1: Convolutional Neural Networks (CNN) & Transfer Learning Lecture 06: NLP and Representation Learning Section 1: Representation Learning and Text Representations Lecture 07: Recurrent Neural Networks Section 1: Recurrent Neural Networks Lecture 08: Attention and Transformers Section 1: Attention Section 1: Attention Section 1 Questions Mot
Tuning hyperparameters for machine learning models like neural networks can be tedious and time-consuming. Traditional grid search or random search lacks efficiency in high-dimensional or non-linear search spaces. Genetic Algorithms (GAs) offer a compelling alternative by navigating the hyperparameter space with adaptive and evolutionary pressure. In this post, we’ll walk through using a Genetic Algorithm
A paper introducing a unified framework combining model size, training time, and data volume to predict neural network performance and establishing the concept of scale-time equivalence
Artificial neural network is a very important part in the new industry of artificial intelligence. In China, there are many researches on artificial neural
A new study reveals the neural dynamics of consciousness. Using AI and advanced imaging in mice, scientists have identified a "conscious variable" and the metastable patterns that define the awake brain
NeuroBE: Escalating neural network approximations of Bucket EliminationSakshi Agarwal, Kalev Kask, Alex Ihler, Rina DechterA major limiting fa
In an increasingly data-rich world the need for developing computing systems that cannot only process, but ideally also interpret big data is becoming continuously more pressing. Brain-inspired concepts have shown great promise towards addressing this need. Here we demonstrate unsupervised learning in a probabilistic neural network that utilizes metal-oxide memristive devices as multi-state synapses. Our approach can be exploited for processing unlabelled data and can adapt to time-varying clusters that und
# Overview We are interested in understanding computations in neural circuits of the mammalian brain. To attack this problem we work at the interface between cellular and systems neuroscience: we aim to understand the cellular toolkit that enables single neurons to perform computations, and in turn how single neurons and their patterns of connections contribute to the computations performed by neural circuits. Our lab has a special focus on neuronal dendrites, which actively transform synaptic inputs into
Abstract page for arXiv paper 2006.04647: Neural Architecture Search without Training
← Back to homepage What is a Learning Rate in a Neural Network? November 6, 2019 by Chris When creating deep learning models, you often have to configure a learning rate when setting the model's hyperparameters, i.e. when you are configuring your neural network. Every time you do that, you might actually wonder like me at first about this: what is a learning rate? Why is it there? And how can you configure it? We'll take a look at these questions in this blog post. This requires that we'll take a look at