Convolutional Neural Networks have revolutionized artificial intelligence by enabling machines to process visual data with remarkable accuracy. Inspired by the visual cortex, CNNs evolved from early models like LeNet-5 to powerful architectures such as AlexNet, VGG, ResNet, and DenseNet. This article explores CNN core concepts, key innovations, real-world applications, and future trends, highlighting their enduring impact on AI.

This article explores how AI is transforming science and medicine in 2025. From breakthroughs in protein engineering and brain mapping to outperforming doctors in clinical diagnosis, AI is becoming an active research partner and clinical assistant. It highlights key findings from Stanford’s AI Index Report, including the rise of virtual labs, predictive healthcare models, AI scribes, and the importance of ethical, inclusive, and regulated deployment.

Discover how neural networks power modern AI, from deep learning to generative models, shaping the future of technology and innovation.

A clustering-based method balances web-scraped datasets, improving AI model performance by ensuring diverse and uniform data representation.

Recent years have birthed a dangerous usage of the technology offered by neural networks, the deep fakes. We'll explore them in this article

Let's explore Generative Adversarial Networks (GANs), one of the pioneers of Gen AI that was capable of producing photorealistic images.

In this article we will go through a brief review of diffusion models, how they work, what their usages are and what are the existing tools.

This article introduces MLFlow and shows you how this great tool can help you boost your productivity in an experimentation environment.