Adversarial Training: Optimization and Applications

Related lectures (31)

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

Discusses language models, focusing on fixed-context neural models and recurrent neural networks.

Explores neural network training, optimization, and environmental considerations, with insights into PCA and K-means clustering.

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Covers a SWOT analysis of Machine Learning and Artificial Intelligence, exploring strengths, weaknesses, opportunities, and threats in the field.

Explores the multilayer perceptron model, training, optimization, data preprocessing, activation functions, backpropagation, and regularization.

Introduces Convolutional Neural Networks, explaining their architecture, training process, and applications in semantic segmentation tasks.

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

Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.

Covers the fundamentals of multilayer neural networks and deep learning.