A deep learning framework combines convolutional and bidirectional recurrent networks to improve protein function prediction from genomic sequences. By automating feature extraction and capturing long ...

Overview: Master deep learning with these 10 essential books blending math, code, and real-world AI applications for lasting ...

Overview:  AI is transforming every industry, making skills like machine learning, data science, and automation essential for ...

Abstract: Convolutional neural networks (CNNs) have emerged as a preferred approach for medical image analysis. The dimensionality of images is a principal factor in CNN models, as they are designed ...

Bridging communication gaps between hearing and hearing-impaired individuals is an important challenge in assistive technology and inclusive education. In an attempt to close that gap, I developed a ...

Understand the Maths behind Backpropagation in Neural Networks. In this video, we will derive the equations for the Back Propagation in Neural Networks. In this video, we are using using binary ...

Artificial intelligence systems that are designed with a biologically inspired architecture can simulate human brain activity before ever being trained on any data, according to new research from ...

Explore 20 different activation functions for deep neural networks, with Python examples including ELU, ReLU, Leaky-ReLU, Sigmoid, and more. #ActivationFunctions #DeepLearning #Python As shutdown ...

1 Institute of Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia 2 Department of Learning, Data Analytics and Technology, Section Cognition, Data and ...

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 ...