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Related lectures (32)
Deep Learning: Data Representations and Neural Networks
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Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.
Neural Networks: Regression and Classification
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Explores neural networks for regression and classification tasks, covering training, regularization, and practical examples.
Deep Learning: Data Representations and Neural Networks
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Explores data representations, histograms, neural networks, and deep learning concepts.
Feed-forward Networks
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Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.
Neural Networks: Basics and Applications
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Explores neural networks basics, XOR problem, classification, and practical applications like weather data prediction.
Deep Neural Networks
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Covers the back-propagation algorithm for deep neural networks and the importance of locality in CNN.
Neural Networks: Perceptron Model and Backpropagation Algorithm
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Covers the perceptron model and backpropagation algorithm in neural networks.
Neural Networks Recap: Activation Functions
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Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.
Neural Networks for NLP
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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.
Neural Network Approximation and Learning
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Delves into neural network approximation, supervised learning, challenges in high-dimensional learning, and deep learning experimental revolution.
Deep Learning: Convolutional Networks
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Explores convolutional neural networks, backpropagation, and stochastic gradient descent in deep learning.
Multi-layer Neural Networks
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Covers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.
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