Unsupervised Deep Learning in Python
Autoencoders, Restricted Boltzmann Machines, Deep Neural Networks, t-SNE and PCA
4.72 (2395 reviews)
25,391
students
10 hours
content
Mar 2025
last update
$29.99
regular price
What you will learn
Understand the theory behind principal components analysis (PCA)
Know why PCA is useful for dimensionality reduction, visualization, de-correlation, and denoising
Derive the PCA algorithm by hand
Write the code for PCA
Understand the theory behind t-SNE
Use t-SNE in code
Understand the limitations of PCA and t-SNE
Understand the theory behind autoencoders
Write an autoencoder in Theano and Tensorflow
Understand how stacked autoencoders are used in deep learning
Write a stacked denoising autoencoder in Theano and Tensorflow
Understand the theory behind restricted Boltzmann machines (RBMs)
Understand why RBMs are hard to train
Understand the contrastive divergence algorithm to train RBMs
Write your own RBM and deep belief network (DBN) in Theano and Tensorflow
Visualize and interpret the features learned by autoencoders and RBMs
Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion
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846480
udemy ID
5/11/2016
course created date
11/20/2019
course indexed date
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