The South African woman who studied biomedical engineering in Japan and launched tech startup backed by Microsoft

After completing a Master’s degree in biomedical engineering in Japan, Pelonomi Moiloa returned to South Africa to launch ...
Hosted on MSN

Neural network Python from scratch with softmax

Implement Neural Network in Python from Scratch ! In this video, we will implement MultClass Classification with Softmax by making a Neural Network in Python from Scratch. We will not use any build in ...
Hosted on MSN

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 ...
pv magazine International

PV module fault diagnosis uses convolutional neural network

Researchers in China have created a dataset of various PV faults and normalized it to accommodate different array sizes and typologies. After testing the new approach in combination with the 1D-CNN ...
C&EN

NeuralTSNE: A Python Package for the Dimensionality Reduction of Molecular Dynamics Data Using Neural Networks

Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, Grudziądzka 5, 87-100 Toruń, Poland ...
IEEE

Improving accuracy in Text Extraction From images using Region-Based Convolutional Neural Networks algorithm compared to Convolutional Neural Network algorithm

Abstract: Text extraction is a critical task in data processing and analysis, requiring high accuracy for effective applications. This research employed a publicly accessible Kaggle dataset to assess ...
IEEE

Weather Image Recognition Based on Convolutional Neural Network and Transfer Learning

Abstract: This paper introduces a groundbreaking approach to weather phenomena classification, presenting a novel deep neural network design (Resnet 50 with transfer learning). Simultaneously, we ...
GitHub

convolution_neural_network.py

self, conv1_get, size_p1, bp_num1, bp_num2, bp_num3, rate_w=0.2, rate_t=0.2 :param conv1_get: [a,c,d], size, number, step of convolution kernel :param size_p1 ...