Deep Learning: Convolutional Networks

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Recurrent Neural Networks: Training and Challenges

Discusses recurrent neural networks, their training challenges, and solutions like LSTMs and GRUs.

Deep Splines: Unifying Framework for Deep Neural Networks

Introduces a functional framework for deep neural networks with adaptive piecewise-linear splines, focusing on biomedical image reconstruction and the challenges of deep splines.

Neural Networks: Regression and Classification

Explores neural networks for regression and classification tasks, covering training, regularization, and practical examples.

Universal Approximation Theorem: MLP

Covers Multi-Layer Perceptrons (MLP) and their application from classification to regression, including the Universal Approximation Theorem and challenges with gradients.

Deep Neural Networks: Training and Optimization

Explores deep neural network training, optimization, preventing overfitting, and different network architectures.

Neural Networks: Training and Optimization

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

Neural Network Approximation and Learning

Delves into neural network approximation, supervised learning, challenges in high-dimensional learning, and deep learning experimental revolution.

Multilayer Perceptron: Training and Optimization

Explores the multilayer perceptron model, training, optimization, data preprocessing, activation functions, backpropagation, and regularization.

Recurrent Neural Networks: Language Detection

Explores language detection using Recurrent Neural Networks and supervised learning concepts.

Crash course on Deep Learning

Covers a crash course on deep learning, including the Mark I Perceptron, neural networks, optimization algorithms, and practical training aspects.