ML

VGGNet is a famous deep learning model used in computer vision—essentially, teaching computers to understand images. It was created by researchers at the Visual Geometry Group (VGG) at the University of Oxford. Since its debut in 2014, VGGNet has become one of the key models that helped advance how machines see and recognize objects in photos. At its core, VGGNet is designed to look at images and decide what is in them.

LeNet-5 is an early and very influential type of convolutional neural network (CNN) developed by Yann LeCun and his colleagues in 1998, designed mainly to recognize handwritten digits like those in the MNIST dataset. What makes LeNet-5 special is how it combines several clever ideas that allow it to efficiently and accurately understand images despite their complexity—ideas that were crucial stepping stones for today’s deep learning revolution.

The journey of pioneers in Machine Learning (ML) and Artificial Intelligence (AI) is a remarkable tale of innovation, collaboration, and the relentless pursuit of knowledge.

Gran Turismo Sophy is an advanced AI racing agent developed through a collaboration between Sony AI, Polyphony Digital, and Sony Interactive Entertainment. This groundbreaking technology utilizes deep reinforcement learning to master the complexities of competitive racing in the Gran Turismo Sport simulator. Initially starting as an AI that struggled to navigate tracks, Sophy has evolved into a formidable competitor capable of challenging top human drivers by mastering racing tactics, etiquette, and vehicle control.

In this blog post, we explore the topic of image generators and their detection techniques. I’ll discuss various methods for detecting image generators and their manipulations. These include analyzing the visual content of an image, examining its metadata, and using machine learning algorithms to identify patterns in the data.

Vision Transformers (ViTs) are redefining image recognition by using Transformer models to capture global context, unlike traditional Convolutional Neural Networks (CNNs) that focus on local features. ViTs excel with large datasets and show impressive scalability and performance.

ImageNet is more than just a dataset. The sheer scale of ImageNet, combined with its detailed labeling, made it essentially the backbone of Computer Vision.

This blog post explains how self-attention and softmax function in Transformer models, crucial for modern NLP. It breaks down how self-attention helps models understand relationships between tokens and how softmax ensures efficient computation and numerical stability.

Generative AI has come a long way, producing stunning images from simple text prompts. But how do Diffusion and Auto-Regressive models work, and why are diffusion models preferred.

In 2012, the field of artificial intelligence witnessed a seismic shift. The catalyst for this transformation was a deep learning model known as AlexNet.