Unsupervised Deep Learning in Python free download

Unsupervised Deep Learning in Python is a deep learning tutorial in Python . Learn the theory behind principal components analysis (PCA) and t-SNE . Derive the PCA algorithm by hand and write the code for PCA . Understand how stacked autoencoders are used in deep learning . Understand why RBMs are hard to train and why they’re hard to teach . Use the contrastive divergence algorithm to train RBMs and understand the limits of the algorithm . Use t-NE in code and write a stacked denoising autoencoder in Theano and Tensorflow . Learn how to train restricted Boltzmann machines (RBMs)

What you’ll find out in Without supervision Deep Learning in Python

  1. Understand the concept behind primary elements evaluation (PCA)
  2. Know why PCA serves for dimensionality reduction, visualization, de-correlation, as well as denoising
  3. Derive the PCA algorithm by hand
  4. Compose the code for PCA
  5. Understand the concept behind t-SNE
  6. Use t-SNE in code
  7. Understand the restrictions of PCA and also t-SNE
  8. Recognize the theory behind autoencoders
  9. Compose an autoencoder in Theano and also Tensorflow
  10. Understand how stacked autoencoders are utilized in deep understanding
  11. Create a piled denoising autoencoder in Theano and also Tensorflow
  12. Understand the concept behind limited Boltzmann devices (RBMs)
  13. Understand why RBMs are difficult to educate
  14. Understand the contrastive divergence formula to train RBMs
  15. Compose your very own RBM and deep belief network (DBN) in Theano and also Tensorflow
  16. Visualize and also interpret the features found out by autoencoders and RBMs

Description

This training course is the following sensible action in my deep knowing, information science, as well as artificial intelligence series. I’ve done a great deal of training courses concerning deep understanding, as well as I simply released a training course regarding not being watched discovering, where I discussed clustering as well as density estimation. So what do you obtain when you place these 2 together? Unsupervised deep learning!

In these training course we’ll begin with some extremely fundamental things – major elements analysis (PCA), and a popular nonlinear dimensionality reduction method referred to as t-SNE (t-distributed stochastic next-door neighbor embedding).

Next, we’ll take a look at an unique sort of unsupervised semantic network called the autoencoder. After describing just how an autoencoder functions, I’ll reveal you just how you can connect a number of them with each other to develop a deep pile of autoencoders, that causes far better efficiency of a supervised deep neural network. Autoencoders are like a non-linear type of PCA.

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
File Name :Unsupervised Deep Learning in Python free download
Content Source:udemy
Genre / Category:Data Science
File Size :1.22 gb
Publisher :Lazy Programmer Team
Updated and Published:07 Jul,2022

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File name: Unsupervised-Deep-Learning-in-Python.rar
File Size:1.22 gb
Course duration:1 hours
Instructor Name:Lazy Programmer Team , Lazy Programmer Inc.
Language:English
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