The Mathematics Behind Neural Networks Detailed Derivation of Key Formulas
Neural networks are the backbone of modern artificial intelligence (AI), powering applications like image recognition, natural language processing, and autonomous vehicles,
Neural networks are the backbone of modern artificial intelligence (AI), powering applications like image recognition, natural language processing, and autonomous vehicles,
Artificial Neural Networks (ANNs) are one of the most important advancements in the field of machine learning, inspired by the biological neural networks in the human brain.
Convolutional Neural Networks (CNNs) are one of the most powerful tools in deep learning, particularly for tasks related to computer vision.
Convolutional Neural Networks (CNNs) have become the go-to architecture for image-related tasks in deep learning.
Large Language Models (LLMs) such as GPT (Generative Pre-trained Transformer) are a class of deep learning models that have revolutionized natural language processing (NLP).
Graphics Processing Units (GPUs) have become the backbone of modern computing, powering everything from gaming to artificial intelligence (AI).
NVIDIA has firmly established itself as one of the most influential companies in the tech industry, particularly known for its leadership in the world of graphics processing units (GPUs).
Let’s break down AI, Machine Learning (ML), and Neural Networks in a structured way, covering key concepts, types of ML, and model architectures like Transformers, and their applications.
Machine Learning
is a vast and intricate field that requires an understanding of key concepts from mathematics, statistics, programming, and data science. Let’s go through everything step-by-step, from the fundamental maths to the essential skills required to build ML models.
In the ever-evolving field of Natural Language Processing (NLP), the introduction of transformers has marked a major turning point.