Multi-layered Perceptron: History and Training Algorithm

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Page 3 of 4

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

Introduces Convolutional Neural Networks, explaining their architecture, training process, and applications in semantic segmentation tasks.

Covers data representation, MLP training, activation functions, and gradient-based learning in deep neural networks.

Covers the basics of neural networks, including the perceptron model and backpropagation.

Covers the perceptron model and backpropagation algorithm in neural networks.

Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.

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

Covers tensors, loss functions, autograd, and convolutional layers in deep learning.

Introduces Multi-Layer Perceptrons (MLPs) and covers logistic regression, reformulation, gradient descent, AdaBoost, and practical applications.

Discusses edge detection techniques and the evolution of deep learning in neural networks.