Deep Learning: Generalization and Optimization

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Page 2 of 4

Covers the fundamentals of deep learning, including data representations, bag of words, data pre-processing, artificial neural networks, and convolutional neural networks.

Deep Learning Fundamentals

Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.

Landscape and Generalisation in Deep Learning

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

Machine Learning for Feature Extraction

Explores machine learning for feature extraction, 3D vision, and neural networks in mobile robotics.

Deep Learning: No Free Lunch Theorem and Inductive Bias

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

Deep Learning for Autonomous Vehicles: Learning

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

Deep Learning: Data Representations and Neural Networks

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

Deep and Convolutional Networks: Generalization and Optimization

Explores deep and convolutional networks, covering generalization, optimization, and practical applications in machine learning.

Convolutional Neural Networks

Covers Convolutional Neural Networks, including layers, training strategies, standard architectures, tasks like semantic segmentation, and deep learning tricks.

Neural Networks: Training and Activation

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