Convolutional Neural Networks

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

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

Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.

Covers the basics of Convolutional Neural Networks, including training optimization, layer structure, and potential pitfalls of summary statistics.

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

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.

Covers convolutional neural networks, filter operations, and their applications in signal processing and image analysis.

Explores convolutional neural networks, backpropagation, and stochastic gradient descent in deep learning.

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

Covers fundamental principles, opportunities, and challenges in machine learning.