Unsupervised Deep Learning in Python from Udemy

Overview

Are you ready for the next step in the deep learning, data science, and machine learning series? This course is perfect for you. Delving into unsupervised deep learning, you’ll start by exploring principal components analysis (PCA) and t-SNE (t-distributed stochastic neighbor embedding), both popular techniques for dimensionality reduction. After mastering these concepts, prepare to dive into the world of autoencoders – a special type of unsupervised neural network. From understanding how autoencoders work to forming a deep stack of autoencoders for better supervised deep neural network performance, you’re about to gain some serious deep-learning knowledge.

But wait, there’s more! You’ll also study restricted Boltzmann machines (RBMs), another popular unsupervised neural network. You’ll learn about Gibbs sampling, a special case of Markov Chain Monte Carlo, and Contrastive Divergence or CD-k methods. Finally, discover how PCA and t-SNE can be used to visualize the features learned by autoencoders and RBMs to find patterns even without labels. This course does require prior understanding of calculus, linear algebra, and Python coding, as well as knowledge of Numpy, Theano, and Tensorflow. Aimed at those interested in more than just using a library/API, this course focuses on building and understanding deep learning models from scratch, giving you a thorough and in-depth understanding of the concepts involved.

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Skills you’ll learn

1. Principal components analysis (PCA)
2. t-SNE (t-distributed stochastic neighbor embedding)
3. Autoencoder understanding and implementation
4. Restricted Boltzmann machines (RBMs) and pretraining
5. Gibbs sampling and Contrastive Divergence (CD-k)
6. Understanding and minimizing free energy
7. Visualization and feature interpretation using PCA and t-SNE
8. Building and understanding deep learning models from scratch

Summary

Dive into the intriguing world of unsupervised deep learning with an exceptional course offered by the proficient Lazy Programmer Team from Lazy Programmer Inc. With an impressive assortment of 88 comprehensive lessons, this highly-rated course (4.7 out of 5) has already captivated more than 20,000 eager learners, equipping them with the skills to excel in Python.