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- 51% Unsupervised Deep Learning in Python

Unsupervised Deep Learning in Python

$16.99Track price

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8.6/10 (Our Score)
Product is rated as #111 in category Machine Learning

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).

Next, we ll look at a special type of unsupervised neural network called the autoencoder. After describing how an autoencoder works, I ll show you how you can link a bunch of them together to form a deep stack of autoencoders, that leads to better performance of a supervised deep neural network. Autoencoders are like a non–linear form of PCA.

Last, we ll look at restricted Boltzmann machines (RBMs). These are yet another popular unsupervised neural network, that you can use in the same way as autoencoders to pretrain your supervised deep neural network. I ll show you an interesting way of training restricted Boltzmann machines, known as Gibbs sampling, a special case of Markov Chain Monte Carlo, and I ll demonstrate how even though this method is only a rough approximation, it still ends up reducing other cost functions, such as the one used for autoencoders. This method is also known as Contrastive Divergence or CD–k. As in physical systems, we define a concept called free energy and attempt to minimize this quantity.

Instructor Details

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

Specification: Unsupervised Deep Learning in Python

Duration

10.5 hours

Year

2020

Level

Intermediate

Certificate

Yes

Quizzes

No

2 reviews for Unsupervised Deep Learning in Python

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  1. Vikram Hegde

    Yes exactly what I was looking for.

    Helpful(0) Unhelpful(0)You have already voted this
  2. Andrea Perlato

    Well explained!

    Helpful(0) Unhelpful(0)You have already voted this

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    Unsupervised Deep Learning in Python

    $16.99

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