In this article, we continue our look at the theory behind recent works on transferring the capabilities of 2D diffusion models to create 3D-Aware Generative Diffusion models.
In this article, we will go through the StyleGAN paper to see how it works and understand it in depth.
In this theory we cover the background theory behind a variety of methodologies for abstractive text summarization
In this article, we go over Neural Machine Translation with Bahdanau and Luong Attention, and demonstrate the value of the innovative model architecture.
In this article we look at the Style Space in StyleGAN models - a way for us to understand and identify style channels that control both local semantic regions and specific attributes defined by positive samples
In this blog post we examine the growing technology of weakly supervised learning, in the context of other machine/deep learning techniques, and discuss some of the potential applications and frameworks that make use of them.
In this blog post we take an in depth look at the Transformer model architecture, and demo its functionality by rebuilding the model from scratch in Python.
In this blog post we explore the differences between deed-forward and feedback neural networks, look at CNNs and RNNs, examine popular examples of Neural Network architectures, and their use cases.
Autonomous vehicles are on of the most exciting, up-and-coming applications of deep learning to hit the public. In this guide, you will learn about the theory behind these vehicles and the relevant ML tools leveraged to make them work.
