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Sunday, January 21, 2018

Unsupervised Deep Learning in Python

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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