Robustness and Diffusion Models

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Neural Networks: Basics and Applications

Explores neural networks basics, XOR problem, classification, and practical applications like weather data prediction.

Neural Networks: Training and Activation

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Provable and Generalizable Robustness in Deep Learning

Explores adversarial examples, defenses, and certifiable robustness in deep learning, including Gaussian smoothing and perceptual attacks.

Diffusion Models and Robustness

Explores diffusion models, GAN training challenges, SDE-based generative models, and robustness in deep learning.

Building Physical Neural Networks

Discusses challenges in building physical neural networks, focusing on depth, connections, and trainability.

Kernel Methods: Neural Networks

Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.

Deep Learning

Covers the fundamentals of deep learning, including data representations, bag of words, data pre-processing, artificial neural networks, and convolutional neural networks.

Multi-layer Neural Networks

Covers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.

Deep Learning: Convolutional Neural Networks and Training Techniques

Discusses convolutional neural networks, their architecture, training techniques, and challenges like adversarial examples in deep learning.

Deep Neural Networks: Training and Optimization

Explores deep neural network training, optimization, preventing overfitting, and different network architectures.