Dimensionality Reduction: PCA and Autoencoders

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Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Introduces the basics of machine learning, covering supervised classification, logistic regression, and maximizing the margin.

Covers PCA, finding directions of largest variance, data dimensionality reduction, and limitations of PCA.

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

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

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

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.

Delves into neural network approximation, supervised learning, challenges in high-dimensional learning, and deep learning experimental revolution.

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

Covers the application of Principal Component Analysis in facial recognition using a famous faces dataset.