Unsupervised Deep Learning in Python, Theano / Tensorflow: Autoencoders, Restricted Boltzmann Machines, Deep Neural Networks, t-SNE and PCA
Created by Lazy Programmer Inc.
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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
Learn how to create state of the art neural networks for deep learning with Facebook's PyTorch Deep Learning library!
Build with modern libraries like Tensorflow, Theano, Keras, PyTorch, CNTK, MXNet. Train faster with GPU on AWS.
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