Hyperdimensional Computing (HDC) Fully Explained
Reshaping the landscape of artificial intelligence and beyond!

What you will 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
Why take this course?
π Course Title: Hyperdimensional Computing (HDC) Fully Explained
π Course Headline: Reshaping the landscape of artificial intelligence and beyond!
π° Course Description: Dive into the world of Hyperdimensional Computing (HDC), an innovative approach to data processing that stands at the forefront of brain-inspired computing. This comprehensive online course, led by the esteemed course instructor Richard Aragon, will take you through the intricate mathematical foundations of HDC and its impactful applications across machine learning, artificial intelligence, and neuroscience.
π What Youβll Learn:
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Foundations of HDC: Gain a solid understanding of high-dimensional binary vectors and their unique mathematical properties that distinguish HDC from traditional neural network and word embedding approaches.
- High-dimensional binary vectors
- Differences between HDC, neural networks, and word embeddings
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Encoding and Decoding in HDC: Learn about deterministic encoding techniques like binding, bundling, and binarization, as well as probabilistic decoding strategies using similarity measures.
- Deterministic encoding techniques
- Probabilistic decoding strategies
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HDC Applications: Develop robust AI models capable of operating in high-noise environments and creating efficient associative memory systems and more.
- Robust classifiers for noisy data
- Associative memory systems
- Advanced topics like hyperdimensional signal processing and sequence encoding
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Practical Implementation: Get hands-on experience with building HDC-based models using Python and libraries such as NumPy and PyTorch. Learn to visualize hypervectors and analyze their performance through dimensionality reduction techniques like PCA (Principal Component Analysis).
- Building HDC models in Python
- Using Python libraries (NumPy, PyTorch)
- Hypervector visualization and PCA analysis
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Real-World Use Cases: Explore the vast array of applications for HDC technology including autonomous systems, natural language processing, healthcare, edge computing, and more.
- Applications in various domains
π©βπ Who Should Enroll: This course is tailored for:
- Engineers interested in brain-inspired computational models
- Researchers seeking alternative approaches to AI
- Technology enthusiasts curious about the future of computing
Basic knowledge of Python programming and linear algebra is recommended but not required, as this course caters to learners at different levels of proficiency.
π Outcomes: Upon completion of this course, you will:
- Master the theory and mechanics of Hyperdimensional Computing.
- Be prepared to design and deploy HDC-based systems.
- Gain valuable insights into the advancements and future potential of brain-inspired computing.
Are you ready to join the vanguard of AI innovation? Enroll now and embark on a journey to reshape the landscape of artificial intelligence and beyond with Hyperdimensional Computing! π
π Enrollment is open - Secure your spot today and transform your approach to computing and AI!
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