# Building Recurrent Neural Networks Using TensorFlow Published 2018-07-11 by Kevin Feasel Ahmet Taspinar walks us through creating a recurrent neural network topology using TensorFlow : As we have also seen in the previous blog posts, our Neural Network consists of a tf.Graph() and a tf.Session(). The tf.Graph() contains all of the computational steps required for the Neural Network, and the tf.Session is used to execute these steps. The computational steps defined in the tf.Graph can be divided into f
Currently, we do not have a good theoretical understanding of how or why neural networks actually work. For example, we know that large neural networ
Convolutional Neural Networks (CNNs) enable effective video feature extraction by processing spatial patterns in individ
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Tildes Sidebar ~tech Log in 12 votes Researchers isolate memorization from problem-solving in AI neural networks Posted November 12, 2025 by Chiasmic Tags: artificial intelligence , research , language models.large , neural networks , memory , logic , researchers , studies , author.benj edwards , source.ars technica https://arstechnica.com/ai/2025/11/study-finds-ai-models-store-memories-and-logic-in-different-neural-regions/ Link information This data is scraped automatically and may be incorrect. Published
Bayesian Neural Networks (BNNs) provide a tool to estimate the uncertainty of a neural network by considering a distribution over weights and sampling different models for each input. In this paper, we propose a method for uncertainty estimation in neural networks which, instead of considering a distribution over weights, samples outputs of each layer from a corresponding Gaussian distribution, parametrized by the predictions of mean and variance sub-layers. In uncertainty quality estimation experiments, we
In this post, we'll discuss recurrent neural network. We'll cover the types of neural networks, how they work, use cases, and best practices
# Use of Neural Networks for Empirical Data Asked by Domenico Tatone (teacher), Mayfield Secondary School on Friday May 3, 1996: > I am currently working on a thesis on group dynamics. In my > attempt to quantify qualitative research (i.e. interpret responses to > interview questions), I am resorting to the development of neural > networks. My question relates to the utility of neural networks in > empirical studies. Could you direct me to resources that would enable > me to implement the proper formation
In short, feature scaling or normalization is not strictly required for neural networks, but it is highly recommended. Scaling or normalizing the input features can be the difference between a neural network that converges in a few iterations and one that takes hundreds of iterations to converge or even fails to converge at all. The optimization process may become slower because the gradients in the direction of the larger-scale features will be significantly larger than the gradients in the direction of th
Experiments and discussions around concatenated context-selected linear mapping systems suggest several constraints that may be relevant to biological neural networks. https://archive.org/details/lessons-from-concatenat