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- 47% Deep Learning: GANs and Variational Autoencoders

Deep Learning: GANs and Variational Autoencoders

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8.5/10 (Our Score)
Product is rated as #129 in category Machine Learning

Variational autoencoders and GANs have been 2 of the most interesting developments in deep learning and machine learning recently.

Yann LeCun, a deep learning pioneer, has said that the most important development in recent years has been adversarial training, referring to GANs.

GAN stands for generative adversarial network, where 2 neural networks compete with each other.

What is unsupervised learning?

Unsupervised learning means we re not trying to map input data to targets, we re just trying to learn the structure of that input data.

Once we ve learned that structure, we can do some pretty cool things.

One example is generating poetry – we ve done examples of this in the past.

But poetry is a very specific thing, how about writing in general?

If we can learn the structure of language, we can generate any kind of text. In fact, big companies are putting in lots of money to research how the news can be written by machines.

But what if we go back to poetry and take away the words?

Well then we get art, in general.

By learning the structure of art, we can create more art.

How about art as sound?

If we learn the structure of music, we can create new music.

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: Deep Learning: GANs and Variational Autoencoders

Duration

7.5 hours

Year

2021

Level

Intermediate

Certificate

Yes

Quizzes

No

2 reviews for Deep Learning: GANs and Variational Autoencoders

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  1. Salaheddin Alakkari

    Very informative and well structured course.

    Helpful(0) Unhelpful(0)You have already voted this
  2. Agustin Lopez J

    buena introduccion

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    Deep Learning: GANs and Variational Autoencoders
    Deep Learning: GANs and Variational Autoencoders

    $15.99

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