Curse of Dimensionality in Deep Learning

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

Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.

Covers the back-propagation algorithm for deep neural networks and the importance of locality in CNN.

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

Explores the application of statistical physics in understanding deep learning with a focus on neural networks and machine learning challenges.

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Provides insights on gradient-based algorithms, deep learning mysteries, and the challenges of non-convex problems.

Covers auto-encoders for data compression and GANs for data generation.

Explores PCA, Autoencoders, and their applications in dimensionality reduction and data generation.

Explores the gap between AI and human intelligence through neuroscience-inspired models and algorithms.