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Related lectures (34)
Neural Networks: Regression and Classification
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Explores neural networks for regression and classification tasks, covering training, regularization, and practical examples.
Neural Networks: Perceptron Model and Backpropagation Algorithm
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Covers the perceptron model and backpropagation algorithm in neural networks.
Convolutional Neural Networks: Fundamentals
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Covers the basics of Convolutional Neural Networks, including training optimization, layer structure, and potential pitfalls of summary statistics.
Long Short-Term Memory Networks
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Introduces Long Short-Term Memory (LSTM) networks as a solution to vanishing and exploding gradients in recurrent neural networks.
Feed-forward Networks
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Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.
Relative Stability Towards Diffeomorphisms in Deep Nets
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Explores the impact of relative stability towards diffeomorphisms in deep neural networks and its correlation with performance.
Deep Neural Networks and Splines
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Covers the fundamentals of deep neural networks and splines, exploring their properties, implications, and applications in modern machine learning.
Deep Learning: Convolutional Neural Networks and Training Techniques
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Discusses convolutional neural networks, their architecture, training techniques, and challenges like adversarial examples in deep learning.
Neural Networks: Basics and Applications
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Explores neural networks basics, XOR problem, classification, and practical applications like weather data prediction.
Monotonicity Criteria in Differentiable Functions
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Explores monotonicity criteria, L'Hopital's rule, and Lipschitz continuity in differentiable functions and deep neural networks.
Multi-layer Neural Networks
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Covers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.
Graph Search: Neural Networks and Deep Learning
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Delves into graph search, neural networks, and deep learning, covering topics like convolutional neural networks and artificial neural networks.
Improving Models of the Ventral Visual Pathway
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Explores computational models of the ventral visual system, focusing on optimizing networks for real-world tasks and comparing to brain data.
Deep Learning: Multilayer Perceptron and Training
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Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.
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