Displaying 266 resources
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Convolutional Neural Networks Lecture
Convolutional Neural Networks form the backbone of current AI revolution and are used in a multitude of classification and regression problems. This lecture overviews the transition from multilayer perceptrons to deep architectures.
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Multilayer perceptron. Backpropagation
This lecture covers the basic concepts and architectures of Multi-Layer Perceptron (MLP), Activation functions, and Universal Approximation Theorem.
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Attention and Transformers Networks
In this lecture, the limitations of Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) in effectively processing sequences are emphasized.
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Adversarial Machine Learning
This lecture overviews Adversarial Machine Learning that has many applications in DNN robustness and in privacy protection.
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Fast 1D Convolution Algorithms
1D convolutions are extensively used in digital signal processing (filtering/denoising) and analysis (also through CNNs). As their computational complexity is of the order O(N^2), their fast execution is a must.
This lecture will overview
Certification
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Tutorial/How To/Guides
Fast 3D Convolution algorithms
This lecture overviews Fast 3D Convolution algorithms that has many applications in the fast implementation of 3D image and video filtering, 3D CNNs and motion estimation.