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- 63% Advanced AI: Deep Reinforcement Learning in Python

Advanced AI: Deep Reinforcement Learning in Python

$10.99Track price

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8.5/10 (Our Score)
Product is rated as #43 in category Artificial Intelligence

This course is all about the application of deep learning and neural networks to reinforcement learning.

If you ve taken my first reinforcement learning class, then you know that reinforcement learning is on the bleeding edge of what we can do with AI.

Specifically, the combination of deep learning with reinforcement learning has led to AlphaGo beating a world champion in the strategy game Go, it has led to self–driving cars, and it has led to machines that can play video games at a superhuman level.

Reinforcement learning has been around since the 70s but none of this has been possible until now.

The world is changing at a very fast pace. The state of California is changing their regulations so that self–driving car companies can test their cars without a human in the car to supervise.

We ve seen that reinforcement learning is an entirely different kind of machine learning than supervised and unsupervised learning.

Supervised and unsupervised machine learning algorithms are for analyzing and making predictions about data, whereas reinforcement learning is about training an agent to interact with an environment and maximize its reward.

Unlike supervised and unsupervised learning algorithms, reinforcement learning agents have an impetus – they want to reach a goal.

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: Advanced AI: Deep Reinforcement Learning in Python

Duration

10.5 hours

Year

2020

Level

Expert

Certificate

Yes

Quizzes

No

6 reviews for Advanced AI: Deep Reinforcement Learning in Python

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  1. Mayank M

    good one

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  2. Phumudzo Vusani Neluheni

    I’m glad for the experience as I’m now very familiar with Reinforcement Learning

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  3. Chow Kong Ming

    Sensitive and understandable

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  4. Unnat Antani

    Much better course than other courses on similar subject. The code base is always up to date so the user doesn’t have to sit and painstakingly debug the code due to out dated versions of libraries used.

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  5. 217143 Piyush Jain

    great introduction and guidelines before proceeding in the cource. straight forward and to the point.

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  6. Katherine F Jiang

    excellent

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    Advanced AI: Deep Reinforcement Learning in Python
    Advanced AI: Deep Reinforcement Learning in Python

    $10.99

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