DiaryFolio

Quantization is a powerful technique used in deep learning to reduce the memory and computational requirements of neural networks by representing weights and activations with fewer bits. In this section, we'll delve into the concept of quantization, elucidating its significance and showcasing its application through examples and diagrams. Understanding Quantization: Quantization involves approximating the floating-point parameters of a neural network with fixed-point or integer representations. By reducing the precision of these parameters, quantization enables the compression of model size and accelerates inference speed, making deep learning models more efficient and deployable on resource-constrained devices. The Process of Quantization: The quantization process typically consists of two main steps: Weight Quantization : In weight quantization, the floating-point weights of the neural network are converted into fixed-point or integer representations with reduced precision. This

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