Manifolds

Manifold Hypothesis[1] Real-world high dimensional data (such as images) lie on low-dimensional manifolds embedded in the high-dimensional space. Chapter 14.6 Deep Learning Book[3] Like many other machine learning algorithms, autoencoders exploit the idea that data concentrates around a low-dimentional manifold or a small set of such manifolds. Some machine learning algorithms exploit this ideal only …

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Autoencoders

Mitesh M. Khapra Lecture PDF Introduction to Autoencoders (Video) Link between PCA and Autoencoders (Video) Regularization in autoencoders (Motivation) (Video) Denoising Autoencoders (Video) Sparse Autoencoders (Video) Contractive autoencoders (Video) Hugo Larochelle Autoencoder – definition (Video) Autoencoder – loss function (Video) Example (Video) Linear Autoencoder (Video) Autoencoder – undercomplete vs. overcomplete hidden layer (Video) Autoencoder – …

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Computer Vision Resources

Conferences: The Computer Vision Foundation – Link Open Access of Papers (The Computer Vision Foundation) – Link Conferences: CVPR – Conference on Computer Vision and Pattern Recognition  WACV – Workshop on Applications of Computer Vision ICCV – International Conference on Computer Vision Topics to explore: Point Cloud What is point cloud – Link Point Cloud …

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Deep Learning Resources

Courses Deep Learning Specialization by Andrew Ng at Coursera – Link Intro to Deep Learning with PyTorch – Facebook AI – Udacity – Link Deep Learning Nanodegree – Udacity – Link – Github Link Books Dive into Deep Learning (Online Book) – Link Deep Learning book by Ian Goodfellow et. al. – Link Important Papers Why Does …

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