By introducing the attention mechanism to the traditional encoder-decoder architecture, the neural network gains the ability to focus on specific parts of the input sequence, enabling enhanced understanding and improved translation accuracy. In this article, we delve into the working principles of attention models and highlight their significant advantages over traditional models without going into mathematical rigor.

In the realm of large language models, the encoder-decoder architecture serves as a fundamental framework. In this essay, we will explore the key aspects of this architecture, including an overview, training methodology, and the process of producing text from a trained model during serving time. Additionally, we will delve into the internal mechanisms of the encoder and decoder stages, discuss training techniques, and highlight the transition from traditional recurrent neural networks (RNNs) to transformer blocks in modern language models.

Image generation has become an intriguing field, and diffusion models are making remarkable strides in this domain. With approaches like variational autoencoders, generative adversarial models (GANs), and auto regressive models, deep learning techniques are mimicking and creating visual content. Let’s dive into this beginner’s guide to understand the inner workings and potential of diffusion models for image generation.

In this article, we explore the significance and implications of generative AI and its intersection with large language models, revolutionizing the landscape of artificial intelligence. — GENERATIVE AI We have come a long way from traditional programming to neural networks to generative models. In traditional programming, we used to have to hard code the rules for distinguishing a cat — type, animal; legs, four; ears, two; fur, yes; likes yarn, catnip. In the wave of neural networks, we…