Convolutional Neural Networks: Filters and Channels

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Image Processing Techniques

Covers image processing techniques including noise addition, filtering, and image enhancement using various filters and tools.

Nonlinear Supervised Learning

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

Convolutional Networks: Overview and Architecture

Covers the motivation and architecture of convolutional networks, from LeNet to AlexNet.

Convolutional Neural Networks

Explores convolutional neural networks, covering convolution, cross-correlation, max pooling, layer structure, and examples like LeNet5 and AlexNet.

Neural Networks: Multilayer Learning

Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.

Neural Networks Recap: Activation Functions

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

Dimensionality Reduction: PCA and Autoencoders

Introduces artificial neural networks, CNNs, and dimensionality reduction using PCA and autoencoders.

Convolutional Neural Networks

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

Neural Network Approximation and Learning

Delves into neural network approximation, supervised learning, challenges in high-dimensional learning, and deep learning experimental revolution.

Improving Models of the Ventral Visual Pathway

Explores computational models of the ventral visual system, focusing on optimizing networks for real-world tasks and comparing to brain data.