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Ramit Sethi – I Will Teach You to Be Rich No Guilt No Excuses No B S Just a 6-Week Program That Works (Second Edition)

Ramit Sethi – I Will Teach You to Be Rich No Guilt No Excuses No B S Just a 6-Week Program That Works (Second Edition)
Category: Tutorial
English | Size: 667.48 MB
“A unique voice on money, one singularly attuned to.his generation.” (San Francisco Chronicle)
Buy as many lattes as you want. Spend extravagantly on the things you love. Live your rich life instead of tracking every last expense with Ramit Sethi’s simple, powerful, and effective six-week program for gaining control over your finances.

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

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.