AI

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.

A neural network is like a highly sophisticated, multi-layered calculator that learns from data. It consists of numerous “neurons” (tiny calculators) connected in layers, with each layer performing a unique function to help the network make predictions or decisions.

The beauty of VAEs lies in their ability to generate new samples by randomly sampling vectors from this known region and then passing them through the generator part of our model.

An autoencoder begins its journey by compressing input data into a lower dimension. It then endeavors to reconstruct the original input from this compressed representation.

Softmax stands as a pivotal component in neural network architectures, offering a means to convert raw scores into interpretable probabilities.