Deep learning approaches, particularly convolutional neural networks (CNNs) and other architectures, were used in 49 papers. These models excel at image-based tasks such as land cover classification, ...

In today's data-driven environment, Python has become the mainstream language in the fields of machine learning and data science due to its concise syntax, rich library support, and active community, ...

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 ...

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 ...

Developer tooling provider Anaconda Inc. today announced that it has closed a Series C funding round worth more than $150 million. Insight Partners led the investment with participation from Mubadala ...

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 ...

The authenticity of corn seeds is critical to yields and their market value. The screening of corn ears is an important step in the processing of corn seeds. In order to protect the intellectual ...

Abstract: Due to the strong feature extraction capabilities, convolutional neural networks (CNNs) have been utilized for various tasks, including image recognition, object detection, and natural ...

Abstract: Convolutional neural network (CNN) was widely applied to the data-driven-based fault diagnosis. However, it often needs to artificially transform the signal into a 2-D image with the help of ...