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Related lectures (33)
Deep Learning: Data Representations and Neural Networks
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Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.
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Document Analysis: Topic Modeling
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Explores document analysis, topic modeling, and generative models for data generation in machine learning.
Deep Learning: Convolutional Neural Networks and Training Techniques
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Discusses convolutional neural networks, their architecture, training techniques, and challenges like adversarial examples in deep learning.
Deep Learning
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Covers the fundamentals of deep learning, including data representations, bag of words, data pre-processing, artificial neural networks, and convolutional neural networks.
Deep Learning: Data Representations and Neural Networks
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Explores data representations, histograms, neural networks, and deep learning concepts.
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.
Deep Learning Fundamentals
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Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.
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Deep Generative Models: Part 2
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Explores deep generative models, including mixtures of multinomials, PCA, deep autoencoders, convolutional autoencoders, and GANs.
Machine Learning and Modern AI: SWOT Analysis
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Covers a SWOT analysis of Machine Learning and Artificial Intelligence, exploring strengths, weaknesses, opportunities, and threats in the field.
Gaussian Mixture Models: Data Classification
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Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.
Hand Pose Estimation
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Covers hand pose estimation, regression techniques, and the evolution of image classification models from LeNet to VGG19.
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