Unsupervised Deep Learning in Python

Unsupervised Deep Learning in Python
English | Size: 556.36 MB
Category: CBTs

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 t-SNE (t-distributed stochastic neighbor embedding).
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Packt Publishing – Supervised and Unsupervised Learning with Python

Packt Publishing – Supervised and Unsupervised Learning with Python
English | Size: 407.28 MB
Category: Tutorial

This course takes a concept-based, explanation-focused approach. Each concept is explained and then the exercise or example is implemented.

Build real-world Artificial Intelligence (AI) applications to intelligently interact with the world around you, explore real-world scenarios, and learn about the various algorithms that can be used to build AI applications. Packed with insightful examples and topics such as predictive analytics and deep learning, this course is a must-have for Python developers. [Read more…]

Pluralsight – Building Unsupervised Learning Models with TensorFlow

Pluralsight – Building Unsupervised Learning Models with TensorFlow
English | Size: 340.58 MB
Category: Tutorial;

Unsupervised learning techniques are powerful, but under utilized and often not well understood. In this course, Building Unsupervised Learning Models with TensorFlow, you’ll learn the various characteristics and features of clustering models such as K-means clustering and hierarchical clustering. First, you’ll dive into building a k-means clustering model in TensorFlow. Next, you’ll discover autoencoders in detail, which are a type of artificial neural network used for unsupervised learning. Finally, you’ll explore encodings or representation of data for dimensionality reduction of problems. By the end of this course, you’ll have a better understanding of how you can work with unlabeled data using unsupervised learning techniques. [Read more…]

O’Reilly – Clustering and Unsupervised Learning

O’Reilly – Clustering and Unsupervised Learning
English | Size: 163.25 MB
Category: Tutorioal

This course introduces clustering, a common technique used widely in unsupervised machine learning. The course begins by defining what clustering means through graphical explanations, and describes the common applications of clustering. Next, it explores k-means clustering in detail, including the concepts of distance functions and k-modes; illustrates hierarchical clustering through visual examples of dendrograms, and discusses different types of clustering algorithms. The course ends with a comparison of the performance of different algorithms. An understanding of basic algebra is required and some knowledge of linear algebra will be helpful. [Read more…]