Unsupervised Deep Learning in Python

Theano / Tensorflow: Autoencoders, Restricted Boltzmann Machines, Deep Neural Networks, t-SNE and PCA.

What Will I 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

Includes:

• 5.5 hours on-demand video
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion

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