Committee Machine: Statistical Physics Approach

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Photonic Extreme Learning Machines: Reservoir Computing Techniques

Covers photonic extreme learning machines and reservoir computing, focusing on their architectures, programming techniques, and applications in optical computing.

Neural Taskonomy and Historical Perspectives in Visual Intelligence

Covers Neural Taskonomy, the evolution of neural networks, and historical perspectives in visual intelligence.

Feed-forward Networks

Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.

Multilayer Networks: First Steps

Covers the preparation for deriving the Backprop algorithm in layered networks using multi-layer perceptrons and gradient descent.

Neural Networks: Multilayer Perceptron

Explores the history, models, training, convergence, and limitations of neural networks, including the backpropagation algorithm and universal approximation.

Neural Networks Recap: Activation Functions

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

Deep Learning: Convolutional Neural Networks and Training Techniques

Discusses convolutional neural networks, their architecture, training techniques, and challenges like adversarial examples in deep learning.

Neural Networks for NLP

Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.

Tricks of the Trade in Deep Learning: Aims

Covers practical questions and aims in deep learning, including neuron types, network architecture, optimization, and weight initialization.

Machine Learning Fundamentals

Introduces the basics of machine learning, covering supervised classification, logistic regression, and maximizing the margin.