Why are there so many saddle points?: Loss landscape and optimization methods

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The Hidden Convex Optimization Landscape of Deep Neural Networks

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Non-Convex Optimization: Techniques and Applications

Covers non-convex optimization techniques and their applications in machine learning.

Neural Networks Optimization

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

Neural Networks: Deep Neural Networks

Explores the basics of neural networks, with a focus on deep neural networks and their architecture and training.

Understanding Machine Learning: Exactly Solvable Models

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Structures in Non-Convex Optimization

Delves into structures in non-convex optimization, emphasizing scalable optimization for deep learning.

Neural Networks: Training and Activation

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

Neural Networks: Training and Optimization

Explores the training and optimization of neural networks, addressing challenges like non-convex loss functions and local minima.

Multilayer Neural Networks: Deep Learning

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