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Lecture
Deep Learning Accelerators: Optimization Strategies
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Related lectures (32)
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Principal Component Analysis: Applications and Limitations
Explores the applications and limitations of Principal Component Analysis, including denoising, compression, and regression.
Compression: Prefix-Free Codes
Explains prefix-free codes for efficient data compression and the significance of uniquely decodable codes.
Auto-encoder and GANs
Covers auto-encoders for data compression and GANs for data generation.
Neural Signal Compression
Explores analog-to-digital conversion, neural signal optimization, multichannel architectures, and on-chip compression techniques in neuroengineering.
Second-Order Model Compression
Explores second-order model compression for massive deep neural networks, showcasing compression techniques and their impact on model accuracy.
PCA: Directions of Largest Variance
Covers PCA, finding directions of largest variance, data dimensionality reduction, and limitations of PCA.
Principal Component Analysis: Dimensionality Reduction
Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.
JPEG 2000: Image Compression
Explores image compression principles, focusing on JPEG 2000, covering transform-based coding, quantization, entropy coding, region of interest, error resilience, and software implementations.
JPEG XS & JPEG XL: Next-Gen Image Compression
Explores the cutting-edge JPEG XS and JPEG XL image compression standards, emphasizing their efficiency and versatility in various applications.
Data Compression: Shannon-Fano Algorithm
Explores the Shannon-Fano algorithm for efficient data compression and its applications in lossless and lossy compression techniques.
