Machine learning is an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...

Abstract: Capsule networks (CapsNet) are a pioneering architecture that can encode image features into vectors rather than scalars, addressing the limitations of traditional Convolutional Neural ...

90% accuracy resnet-like CNN from scratch for Intel Image Classification dataset WITHOUT transfer learning and with complex metrics.

PythoC lets you use Python as a C code generator, but with more features and flexibility than Cython provides. Here’s a first look at the new C code generator for Python. Python and C share more than ...

Explore the first part of our series on sleep stage classification using Python, EEG data, and powerful libraries like Sklearn and MNE. Perfect for data scientists and neuroscience enthusiasts!

This project demonstrates image classification using Convolutional Neural Networks (CNNs) in Python with TensorFlow and Keras, trained and tested on the CIFAR-10 dataset. The CIFAR-10 dataset consists ...

Abstract: In this paper we deal with image classification tasks using the powerful CLIP vision-language model. Our goal is to advance the classification performance using the CLIP’s image encoder, by ...

Python libraries are pre-written collections of code designed to simplify programming by providing ready-made functions for specific tasks. They eliminate the need to write repetitive code and cover ...

Introduction: Accurate environmental image classification is essential for ecological monitoring, climate analysis, disaster detection, and sustainable resource management. However, traditional ...

Weed management presents a major challenge to vegetable growth. Accurate identification of weeds is essential for automated weeding. However, the wide variety of weed types and their complex ...