Concept

Rectifier (neural networks)

Related lectures (30)

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Explores supervised learning as an ill-posed problem and the integration of sparse adaptive splines into neural architectures.

Feedforward Neural Networks: Activation Functions and Backpropagation

Introduces feedforward neural networks, activation functions, and backpropagation for training, addressing challenges and powerful methods.

Multilayer Perceptron: Training and Optimization

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Neural Network Approximation and Learning

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Monotonicity Criteria in Differentiable Functions

Explores monotonicity criteria, L'Hopital's rule, and Lipschitz continuity in differentiable functions and deep neural networks.

Statistical Approach: Summary and Quiz

Discusses statistical view of neural networks, classification tasks, and cross-entropy loss functions.

Vanishing Gradient Problem: Deep Learning

Discusses the vanishing gradient problem in deep neural networks and its solutions.

Weight Update: Mean Input and Bias Problem

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Feed-forward Networks

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Splines and Imaging: From Compressed Sensing to Deep Neural Nets

Explores the optimality of splines for imaging and deep neural networks, demonstrating sparsity and global optimality with spline activations.

Deep Learning for NLP

Explores deep learning for NLP, covering word embeddings, context representations, learning techniques, and challenges like vanishing gradients and ethical considerations.

Deep Neural Networks and Splines

Covers the fundamentals of deep neural networks and splines, exploring their properties, implications, and applications in modern machine learning.

Recurrent Neural Networks: Neural Language Models

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Multi-class Problems: One-hot Coding

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Neural Networks for NLP

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Word Embeddings: Context and Representation

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