Hyperdimensional Computing (Hdc) Fully Explained

Reshaping the landscape of artificial intelligence and beyond!

What you'll learn

The architecture of Hyperdimensional Computing and how it mathematically works

The significant differences between HDC architecture and Transformers Architecture

Hands on with Encoders and Decoders and thorough code explanations of all concepts

How to modify Transformers pretrained models to work with HDC embeddings and encoder/decoders

Requirements

This course is mathematically intensive and a basic understanding of Python is required

Description

Unlock the full potential of Hyperdimensional Computing (HDC)—a groundbreaking approach to information processing that mimics the brain's way of encoding and manipulating data. This course provides a comprehensive introduction to HDC, focusing on its mathematical foundations, practical implementations, and transformative applications in machine learning, artificial intelligence, and neuroscience.Through a combination of engaging lectures, hands-on coding exercises, and real-world case studies, participants will explore how high-dimensional representations enable robust, noise-tolerant computation and empower advanced pattern recognition, classification, and associative memory tasks.What You’ll Learn:Foundations of HDC:Understand the principles of high-dimensional binary vectors and their mathematical properties.Learn how HDC differs from traditional approaches like neural networks and word embeddings.Encoding and Decoding in HDC:Discover deterministic encoding techniques such as binding, bundling, and binarization.Explore probabilistic decoding strategies using similarity measures.HDC Applications:Develop robust classifiers for high-noise environments.Implement associative memory systems for fast, efficient data retrieval.Explore advanced topics like hyperdimensional signal processing and sequence encoding.Practical Implementation:Build HDC-based models using Python and popular libraries like NumPy and PyTorch.Visualize hypervectors and analyze their performance using dimensionality reduction techniques like PCA.Real-World Use Cases:Dive into HDC applications in autonomous systems, natural language processing, healthcare, and edge computing.Who Should Enroll:This course is designed for engineers, researchers, and technology enthusiasts who are eager to explore alternative computational paradigms. A basic understanding of Python programming and linear algebra is recommended but not required.Outcomes:By the end of this course, you will:Master the theory and mechanics of HDC.Be equipped to design and deploy HDC-based systems.Gain insights into the future of brain-inspired computing.Join us to explore how Hyperdimensional Computing is reshaping the landscape of artificial intelligence and beyond!

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 A Very Basic Introduction To Hyperdimensional Computing

Lecture 3 Hyper Dimensional Computing (HDC) Encoders vs Transformers Encoders

Lecture 4 Hyper Dimensional Computing (HDC) Decoders vs Transformers Decoders

Lecture 5 Exploring The Math Behind Different Neural Network Architectures

Lecture 6 Hands On With Building An HDC Encoder/Decoder

Anyone who is looking into Transformers alternatives and/or is interested in tech related to GNNs