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 expertise.From neural networks to N ...

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 ...

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 ...

🎮 Train a Deep Q-Learning agent using TensorFlow to master Atari Breakout with efficient experience replay and modular architecture for easy customization.

Visual Attention Networks (VANs) leveraging Large Kernel Attention (LKA) have demonstrated remarkable performance in diverse computer vision tasks, often outperforming Vision Transformers (ViTs) in ...

Abstract: This study investigates the application of Spiking Neural Network (SNN) in seismic signal denoising by developing a Convolutional Neural Network (CNN) to SNN conversion framework. We focus ...

1 College of Electronic Science and Technology, National University of Defense Technology, Changsha, China 2 College of Electronics and Internet of Things, Chongqing Polytechnic University of ...

In this tutorial, we take a hands-on approach to building an advanced convolutional neural network for DNA sequence classification. We focus on simulating real biological tasks, such as promoter ...

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 ...