Deep Generative Models: Variational Autoencoders & GANs

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Page 3 of 3

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

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

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

Covers the fundamentals of multilayer neural networks and deep learning.

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Explores self-supervised learning for autonomous vehicles, deriving labels from data itself and discussing its applications and challenges.

Explores diffusion models, focusing on generating samples from a distribution and the importance of denoising in the process.

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

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

Explores learning in deep learning for autonomous vehicles, covering predictive models, RNN, ImageNet, and transfer learning.