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Smithsonian Ch. – Finding Life in Outer Space (2017)

Smithsonian Ch. – Finding Life in Outer Space (2017)
Category: Documentary
English | Size: 1.40 GB
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Finding Life in Outer Space
Over billions of years, planet Earth has become home to an amazing interdependent ecosystem, containing a dizzying variety of animals and plants. But how did life here begin? And does it exist anywhere outside of our solar system? We uncover the secrets of our world by tracking the evolution of the cosmos itself, from the Big Bang onwards. Follow scientists responsible for some of the major breakthroughs in understanding the origins of life and witness how their discoveries are fundamentally changing the way we perceive the universe.

In this part, you will create one of the most powerful Deep Learning models. We will even go as far as saying that you will create the Deep Learning model closest to “Artificial Intelligence”. Why is that? Because this model will have long-term memory, just like us, humans.

The branch of Deep Learning which facilitates this is Recurrent Neural Networks. Classic RNNs have short memory, and were neither popular nor powerful for this exact reason. But a recent major improvement in Recurrent Neural Networks gave rise to the popularity of LSTMs (Long Short Term Memory RNNs) which has completely changed the playing field. We are extremely excited to include these cutting-edge deep learning methods in our course!

In this part you will learn how to implement this ultra-powerful model,

and we will take the challenge to use it to predict the real Google stock price. A similar challenge has already been faced by researchers at Stanford University and we will aim to do at least as good as them.

According to a recent report published by Markets & Markets the Fraud Detection and Prevention Market is going to be worth $33.19 Billion USD by 2021. This is a huge industry and the demand for advanced Deep Learning skills is only going to grow. That’s why we have included this case study in the course.

This is the first part of Volume 2 – Unsupervised Deep Learning Models. The business challenge here is about detecting fraud in credit card applications. You will be creating a Deep Learning model for a bank and you are given a dataset that contains information on customers applying for an advanced credit card.

This is the data that customers provided when filling the application form. Your task is to detect potential fraud within these applications. That means that by the end of the challenge, you will literally come up with an explicit list of customers who potentially cheated on their applications.