Machine Learning for Feature Extraction

Related lectures (30)

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Covers the fundamentals of multilayer neural networks and deep learning.

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

Covers the implementation of fully connected neural networks on two datasets using PyTorch.

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

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

Covers the No Free Lunch Theorem and the role of inductive bias in deep learning and reinforcement learning.

Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.

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

Covers the basics of Convolutional Neural Networks, including training optimization, layer structure, and potential pitfalls of summary statistics.

Covers the conclusions on deep learning and an introduction to statistical learning theory.