Paul Hand Courses

Continual Learning and Catastrophic Forgetting

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Explore continual learning and catastrophic forgetting in deep neural networks, covering evaluation methods and algorithms based on regularization, dynamic architectures, and Complementary Learning Systems.

• YouTube
• 42 minutes
• On-Demand
• Free Video

Variational Autoencoders

Explore generative models, autoencoders, and variational techniques in deep learning. Learn about architecture, optimization, and practical applications of variational autoencoders.

• YouTube
• 43 minutes
• On-Demand
• Free Video

Gradient Descent and Stochastic Gradient Descent

Explore gradient descent and stochastic gradient descent in deep neural networks, covering convergence rates, learning rate effects, and challenges in optimization algorithms.

• YouTube
• 57 minutes
• On-Demand
• Free Video

Adversarial Examples for Deep Neural Networks

Explore adversarial examples in deep neural networks, covering white/black box attacks, real-world scenarios, and defense strategies like adversarial training. Gain insights into various attack methods and their implications.

• YouTube
• 44 minutes
• On-Demand
• Free Video

Neural Network Architectures for Images

Explore neural network architectures for image processing, including classification, segmentation, and enhancement. Learn about CNNs, ResNets, autoencoders, and their applications in various imaging tasks.

• YouTube
• 47 minutes
• On-Demand
• Free Video

Supervised Machine Learning Review

Comprehensive review of supervised machine learning concepts, focusing on statistical frameworks, loss functions, and the intriguing double-descent phenomenon in neural networks.

• YouTube
• 47 minutes
• On-Demand
• Free Video

Unlearned Neural Networks as Image Priors for Inverse Problems

Explore unlearned neural networks as image priors for inverse problems in imaging, covering Deep Image Prior, Deep Decoder, and Deep Geometric Prior techniques and their applications.

• YouTube
• 50 minutes
• On-Demand
• Free Video

Theory of GANs for Compressed Sensing

Explore the theory of GAN priors in compressed sensing, covering key concepts, proofs, and optimization problems. Gain insights into visual representation and set restricted eigen value conditions.

• YouTube
• 57 minutes
• On-Demand
• Free Video