Unsupervised Deep Learning in Python free download

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 Not being watched Deep Knowing in Python

  1. Understand the concept behind principal elements analysis (PCA)
  2. Know why PCA works for dimensionality decrease, visualization, de-correlation, and denoising
  3. Derive the PCA algorithm by hand
  4. Create the code for PCA
  5. Understand the theory behind t-SNE
  6. Use t-SNE in code
  7. Recognize the limitations of PCA and t-SNE
  8. Understand the concept behind autoencoders
  9. Compose an autoencoder in Theano and also Tensorflow
  10. Understand exactly how piled autoencoders are utilized in deep learning
  11. Write a piled denoising autoencoder in Theano as well as Tensorflow
  12. Understand the concept behind limited Boltzmann devices (RBMs)
  13. Understand why RBMs are hard to educate
  14. Understand the contrastive divergence algorithm to train RBMs
  15. Compose your own RBM and deep belief network (DBN) in Theano and also Tensorflow
  16. Visualize and also analyze the features learned by autoencoders and RBMs

Description

This program is the next rational action in my deep understanding, data science, as well as artificial intelligence series. I’ve done a great deal of training courses about deep discovering, and I just launched a program concerning not being watched learning, where I discussed clustering and also thickness estimate. So what do you obtain when you place these 2 with each other? Unsupervised deep learning!

In these program we’ll start with some very standard things – primary components analysis (PCA), and a prominent nonlinear dimensionality decrease strategy known as t-SNE (t-distributed stochastic neighbor embedding).

Next, we’ll take a look at a special sort of not being watched semantic network called the autoencoder. After describing exactly how an autoencoder functions, I’ll reveal you how you can link a number of them together to form a deep stack of autoencoders, that leads to much better performance of a supervised deep neural network. Autoencoders are like 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 :4.31 gb
Publisher :Lazy Programmer Team
Updated and Published:08 Aug,2022
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File name: Unsupervised-Deep-Learning-in-Python.rar
File Size:4.31 gb
Course duration:7 hours
Instructor Name:Lazy Programmer Team , Lazy Programmer Inc.
Language:English
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