Size: 2.85 GB
Theano / Tensorflow: Autoencoders, Restricted Boltzmann Machines, Deep Neural Networks, tSNE and PCA
What you’ll learn
 Understand the theory behind principal components analysis (PCA)

Know why PCA is useful for dimensionality reduction, visualization, decorrelation, and denoising

Derive the PCA algorithm by hand
 Write the code for PCA
 Understand the theory behind tSNE
 Use tSNE in code
 Understand the limitations of PCA and tSNE
 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
Requirements
 Knowledge of calculus and linear algebra
 Python coding skills
 Some experience with Numpy, Theano, and Tensorflow
 Know how gradient descent is used to train machine learning models
 Install Python, Numpy, and Theano
 Some probability and statistics knowledge
 Code a feedforward neural network in Theano or Tensorflow
Description
This course is the next logical step in my deep learning, data science, and machine learning series. I’ve done a lot of courses about deep learning, and I just released a course about unsupervised learning, where I talked about clustering and density estimation. So what do you get when you put these 2 together? Unsupervised deep learning! In these course we’ll start with some very basic stuff – principal components analysis (PCA), and a popular nonlinear dimensionality reduction technique known as tSNE (tdistributed stochastic neighbor embedding). calculus
 linear algebra
 probability
 Python coding: if/else, loops, lists, dicts, sets
 Numpy coding: matrix and vector operations, loading a CSV file
 can write a feedforward neural network in Theano or Tensorflow
 Watch it at 2x.
 Take handwritten notes. This will drastically increase your ability to retain the information.
 Write down the equations. If you don’t, I guarantee it will just look like gibberish.
 Ask lots of questions on the discussion board. The more the better!
 Realize that most exercises will take you days or weeks to complete.
 Write code yourself, don’t just sit there and look at my code.
 Check out the lecture “What order should I take your courses in?” (available in the Appendix of any of my courses, including the free Numpy course)
Who this course is for:
 Students and professionals looking to enhance their deep learning repertoire
 Students and professionals who want to improve the training capabilities of deep neural networks
 Students and professionals who want to learn about the more modern developments in deep learning
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