Repository for the book Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python. Deep learning is not just the talk of the town among tech folks.

By the end of the book, you’ll pack everything into a complete Python deep learning library, creating your own class hierarchy of layers, activation functions, and neural network architectures ...

There was an error while loading. Please reload this page. Welcome to another tutorial on Keras. This tutorial will be exploring how to build a Convolutional Neural ...

Deep convolutional neural networks (DCNN) were applied to classify different types of ovarian tumors in detail. We used transfer learning on six pre-trained ... images were resized to 256 * 256 pixels ...

This research aims to investigate the adaptability of several existing deep-learning architectures for semantic segmentation in off-road environments. Previous studies of two Convolutional Neural ...

Deep learning neural networks, exemplified by models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Generative Adversarial Networks (GANs), have achieved ...

The proposed work applies a Convolutional Neural Network (CNN) on retinal images to extract ... The proposed model was implemented using Keras, which is a deep learning API written in Python. Keras ...