AI became powerful because of interacting mechanisms: neural networks, backpropagation and reinforcement learning, attention, ...

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...

Something extraordinary has happened, even if we haven’t fully realized it yet: algorithms are now capable of solving ...

The agent acquires a vocabulary of neuro-symbolic concepts for objects, relations, and actions, represented through a ...

Machine learning careers offer strong salary growth across Indian industriesReal projects and deployment skills matter more ...

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 ...

This workshop will consider several applications based on machine learning classification and the training of artificial neural networks and deep learning.

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 ...

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 ...

Abstract: Deep learning has achieved remarkable success across a wide range of applications, such as language modeling, computer vision, recommendation systems, and robotics. However, the growing size ...

CNN in deep learning is a special type of neural network that can understand images and visual information. It works just like human vision: first it detects edges, lines and then recognizes faces and ...