Neural Taskonomy and Historical Perspectives in Visual Intelligence

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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.

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

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

Explores neural networks for regression and classification tasks, covering training, regularization, and practical examples.

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Explores neural networks basics, XOR problem, classification, and practical applications like weather data prediction.

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

Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.

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