DiaryFolio

Tensors are the backbone of modern deep learning, serving as the fundamental data structure for representing and manipulating multi-dimensional data. In this section, we'll explore tensors in greater detail, unraveling their intricate properties and showcasing their versatility through examples. Understanding Tensors: At its core, a tensor is a mathematical object that generalizes scalars, vectors, and matrices to higher dimensions. While  scalars are zero-dimensional (0D) tensors,  vectors are one-dimensional (1D) tensors,  matrices are two-dimensional (2D) tensors.  Tensors extend this concept further, allowing us to represent and manipulate data in three or more dimensions.  This abstraction makes tensors well-suited for capturing the complex relationships present in real-world data, such as images, audio signals, and text. Multiple Dimensions: One of the defining features of tensors is their ability to encapsulate information across multiple dimensions. Consider a simple exampl

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