Neural networks are a core component of modern recommendation systems because they excel at finding patterns in large, c
> **_NOTE:_** This post is part of my [Machine Learning Series](https://eecue.com/blog/machine-learning-series---exploring-the-world-of-ai-ml) where I’m discussing how AI/ML works and how it has evolved over the last few decades. Feedforward Neural Networks (FNNs), also known as Multi-Layer Perceptrons (MLPs), are one of the most fundamental and widely-used neural network architectures in machine learning. FNNs have been employed for a variety of tasks, including classification, regression, and feature
Graph neural network is a special kind of network, which works with a graph as a data sample. The typical neural network works with arrays, while GNN works with graphs
A lot of engineering projects and tutorials related to basic structure of graph neural networks for engineering students, hobbyists and professionals
Neural network architectures that operate on tree-structured data
This post was written in collaboration with Joshua Marie. What Are Bayesian Neural Networks? Standard neural networks learn fixed weights during training and produce a single point estimate for each input — with no sense of how confident the model
# Understanding deep neural networks Posted by Ben Lorica September 12, 2019August 5, 2020 Posted inUncategorizedTags: AI , data show , podcast ### The O’Reilly Data Show Podcast: Michael Mahoney on developing a practical theory for deep learning. In this episode of the Data Show , I speak with Michael Mahoney , a member of RISELab , the International Computer Science Institute , and the Department of Statistics at UC Berkeley. A physicist by training, Mahoney has been at the forefront of many important
's Rushi's Ctrl+AI+Ship Home Musings Tech About Contact Tag: neural-networks Jun 02 2026 0 LLM parameters: what they are and how they actually work Posted by Rushi You’ve seen the numbers. 7B. 70B. 405B. Everyone talks about parameter counts. But what are they? Why does size matter? And what actually happens when you hit “Generate”? This post covers the mechanics: what parameters are, where they live in the model architecture, how scaling affects them, and what that means if you’re running or
# What is eszka doing ## poniedziałek, 2 stycznia 2017 ### LSTM - recurrent neural networks Since september, I was focused on my graduation project. I was working with my two friends and one of the topics was classification of data. We have to recognize gestures from data which was comming from gloves equiped with accelerometer and magnetometer sensors. There are several methods to handle that kind of problem: Dynamic Time Warping, Hidden Markov Models and neural networks. We decided to use LSTMs (Long
# Neural Networks Are Polynomial Regression Norman Matloff announces a new paper : A summary of the paper is: We present a very simple, informal mathematical argument that neural networks (NNs) are in essence polynomial regression (PR). We refer to this as NNAEPR. NNAEPR implies that we can use our knowledge of the “old-fashioned” method of PR to gain insight into how NNs — widely viewed somewhat warily as a “black box” — work inside. One such insight is that the outputs of an NN layer will be