DiaryFolio

Transformers have emerged as a revolutionary class of deep learning models, fundamentally reshaping the landscape of natural language processing (NLP). In this comprehensive section, we'll delve into the intricacies of transformers, exploring their architecture, mechanisms, and groundbreaking applications across various NLP tasks. Understanding Transformers: Transformers represent a paradigm shift in NLP, departing from traditional recurrent neural networks (RNNs) and convolutional neural networks (CNNs). Introduced in the seminal paper " Attention is All You Need " by Vaswani et al., transformers leverage self-attention mechanisms to capture long-range dependencies in sequential data efficiently. This architecture enables transformers to process entire sequences of tokens in parallel, circumventing the limitations of sequential processing in RNNs and CNNs. Key Components of Transformers: Self-Attention Mechanism: At the heart of transformers lies the self-attention mecha

In the ever-evolving landscape of artificial intelligence, Language Models (LMs) stand out as one of the most fascinating and impactful innovations. These LMs have revolutionized various aspects of natural language processing, enabling machines to comprehend and generate human-like text with astonishing accuracy. In this blog post, we'll embark on a journey to demystify LMs, exploring key terminologies and shedding light on their inner workings. The below blog will put a summary Understanding Key Terminologies: 1. Tensors Tensors are fundamental data structures used in deep learning frameworks like TensorFlow and PyTorch. They are multi-dimensional arrays that allow efficient representation of complex data, such as images, text, and numerical data. In the context of LMs, tensors serve as the primary means of storing and manipulating input data, facilitating the training and inference processes. 2. Quantization: Quantization is a technique used to reduce the memory and computation