NFNets: Removing BatchNorm for High-Performance Image Recognition

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Convolutional Neural Networks: Structure & Regularization

Explores Convolutional Neural Networks, data augmentation, weight decay, and dropout for enhancing model performance.

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Graph Search: Neural Networks and Deep Learning

Delves into graph search, neural networks, and deep learning, covering topics like convolutional neural networks and artificial neural networks.

Deep Neural Networks

Covers the back-propagation algorithm for deep neural networks and the importance of locality in CNN.

Deep Learning: Data Representations and Neural Networks

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Deep Learning: No Free Lunch Theorem and Inductive Bias

Covers the No Free Lunch Theorem and the role of inductive bias in deep learning and reinforcement learning.

Multilayer Perceptron: Training and Optimization

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

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

Convolutional Neural Networks

Introduces Convolutional Neural Networks (CNNs) for autonomous vehicles, covering architecture, applications, and regularization techniques.

Neural Networks: Multilayer Perceptrons

Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.