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Overview
Autoencoders, Restricted Boltzmann Machines, Deep Neural Networks, t-SNE and PCA What you'll learn: Understand the theory behind principal components analysis (PCA)Know why PCA is useful for dimensionality reduction, visualization, de-correlation, and denoisingDerive the PCA algorithm by handWrite the code for PCAUnderstand the theory behind t-SNEUse t-SNE in codeUnderstand the limitations of PCA and t-SNEUnderstand the theory behind autoencodersWrite an autoencoder in Theano and TensorflowUnderstand how stacked autoencoders are used in deep learningWrite a stacked denoising autoencoder in Theano and...
Syllabus
- Introduction and Outline
- Principal Components Analysis
- t-SNE (t-distributed Stochastic Neighbor Embedding)
- Autoencoders
- Restricted Boltzmann Machines
- The Vanishing Gradient Problem
- Applications to NLP (Natural Language Processing)
- Applications to Recommender Systems
- Theano and Tensorflow Basics Review
- Appendix / FAQ Finale
Instructor
Lazy Programmer Team
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