Deep neural networks (DNNs) have become a cornerstone of modern AI technology, driving a thriving field of research in ...
This workshop will consider several applications based on machine learning classification and the training of artificial neural networks and deep learning.
Overview: Master deep learning with these 10 essential books blending math, code, and real-world AI applications for lasting ...
This project contains implementations of simple neural network models, including training scripts for PyTorch and Lightning frameworks. The goal is to provide a modular, easy-to-understand codebase ...
Abstract: Several studies have analyzed traffic patterns using Vehicle Detector (VD) and Global Positioning System (GPS) data. VD records the speed of vehicles passing through detectors, GPS data ...
CATS-Net (Concept Abstraction and Task-Solving Network) is a comprehensive framework for understanding and implementing concept abstraction in neural networks. The system combines supervised learning ...
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Build a deep neural network from scratch in Python
We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write the code, but it's entirely from scratch in python. We will code Deep Neural ...
Abstract: This article proposes a neural network (NN)-based calibration framework via quantization code reconstruction to address the critical limitation of multidimensional NNs (MDNNs) in ...
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