Lectures
You can download the lectures here. Big Thanks to Jordan Sanders for agreeing to take notes in class that I can post here.
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Lecture 1 Course Introduction
Description: We will go over course policies, a brief description and introduction
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Lecture 2 Singular Value Decomposition (SVD)
Description: We will go over basics of SVD
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Lecture 4 Image Encoding
Description: Here we will look at computation of bases matrix for face images
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Lecture 5 Principal Component Analysis (PCA)
Description: Here we will discuss dimensionality reduction through SVD decomposition
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Lecture 8 Neural Networks
Description: We will introduce the neural network model for deep learning
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Lecture 9 Autoencoders
Description: We will introduce the concept of Autoencoders
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Lecture 10 Autoencoders II
Description: We will discuss Autoencoders with linear and convolutional layers
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Lecture 12 Probabilistic PCA
Description: We will start the discussion of generative models with Probabilistic PCA
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Lecture 13 Probabilistic PCA II
Description: We will continue with Probabilistic PCA
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Lecture 14 Probabilistic PCA III
Description: We will conclude Probabilistic PCA with code implementation in python
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Lecture 16 Variational Autoencoders II
Description: We will continue discussing Variational Autoencoders
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Lecture 19 Variational Autoencoders III
Description: We will continue discussing Variational Autoencoders
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Lecture 20 Variational Autoencoders IV
Description: We will continue discussing Variational Autoencoders
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Lecture 21 Variational Autoencoders V
Description: We will be discussing Variational Autoencoders with full covariance matrix for the encoder
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Lecture 23 VAE conclusion
Description: We will be concluding VAEs and we will also discuss working with custom datasets
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Lecture 24 Gradient Descent for Deep Learning I
Description: We will be discussing the basics of deriving gradients for optimizing neural networks
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Lecture 25 Gradient Descent for Deep Learning II
Description: We will be concluding the basic discussion on optimizing neural networks
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Lecture 26 Gradient Descent for Deep Learning III
Description: We will implement a regression and classification neural network from scratch in python
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