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Auto-encoder and GANs
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Related lectures (32)
Deep Learning: Data Representations and Neural Networks
Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.
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Explores how modern architectures beat the curse of dimensionality and the importance of stability in deep learning models.
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Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.
Deep Learning: Data Representations and Neural Networks
Explores data representations, histograms, neural networks, and deep learning concepts.
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Covers PCA, Kernel PCA, and autoencoders for dimensionality reduction in data analysis.
Deep Learning: Dimensionality and Data Representation
Delves into deep learning's dimensionality, data representation, and performance in classifying large-dimensional data, exploring the curse of dimensionality and the neural tangent kernel.
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Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.
Machine Learning Fundamentals
Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
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Explores model compression techniques in NLP, discussing pruning, quantization, weight factorization, knowledge distillation, and attention mechanisms.
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