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 discover in Without supervision Deep Knowing in Python

  1. Understand the theory behind principal elements analysis (PCA)
  2. Know why PCA is useful for dimensionality decrease, visualization, de-correlation, as well as denoising
  3. Obtain the PCA formula by hand
  4. Create the code for PCA
  5. Understand the concept behind t-SNE
  6. Usage t-SNE in code
  7. Comprehend the limitations of PCA as well as t-SNE
  8. Understand the theory behind autoencoders
  9. Create an autoencoder in Theano and Tensorflow
  10. Understand just how stacked autoencoders are utilized in deep learning
  11. Create a piled denoising autoencoder in Theano and Tensorflow
  12. Understand the theory behind limited Boltzmann equipments (RBMs)
  13. Understand why RBMs are hard to train
  14. Understand the contrastive divergence formula to train RBMs
  15. Compose your own RBM as well as deep idea network (DBN) in Theano and also Tensorflow
  16. Visualize and translate the functions found out by autoencoders as well as RBMs

Description

This training course is the following logical action in my deep discovering, information scientific research, as well as artificial intelligence collection. I have actually done a great deal of training courses concerning deep knowing, and I just launched a training course about without supervision understanding, where I spoke about clustering and density estimate. So what do you get when you place these 2 with each other? Not being watched deep learning!

In these course we’ll start with some extremely standard stuff – major components analysis (PCA), as well as a popular nonlinear dimensionality decrease method called t-SNE (t-distributed stochastic neighbor embedding).

Next, we’ll take a look at an unique kind of not being watched neural network called the autoencoder. After explaining exactly how an autoencoder functions, I’ll reveal you exactly how you can link a bunch of them with each other to develop a deep pile of autoencoders, that results in better efficiency of a supervised deep neural network. Autoencoders resemble a non-linear kind 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 :3.95 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:3.95 gb
Course duration:6 hours
Instructor Name:Lazy Programmer Team , Lazy Programmer Inc.
Language:English
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