In the recent years, the field of Machine learning has been progressing at an exponential rate. The progress in the last 4 – 5 years has happened primarily because of the decrease in the cost of hardware which has enabled scientists and researchers to develop really powerful Deep Learning (Neural Network) algorithms. Neural Networks were developed back in the 80s. However, the progress can only be attributed to the recent price reduction in hardware. In this blog post, we will talk about how to train a Neural Network in Python.
In order to understand it better, let us first think of a problem statement such as – given a credit card transaction, classify if it is a genuine transaction or a fraud transaction.
A fraud transaction is a transaction where the transaction has happened without the consent of the owner of the credit card. For instance, you may have lost your wallet at a shop. Someone picked it up and made a purchase of $10,000. Clearly, you may not be making such high purchases. Wouldn’t it be great if your bank could somehow identify that this transaction wasn’t done by you and so, they could block it automatically?
Today, with the advancements in Machine Learning, such things are indeed possible and in fact, they have been implemented by some of the top banks globally.
In order to be a bit more specific, let us also try to understand – what comprises of a transaction?
Clearly, there is a pattern in the data – the “features” of the transaction indicate the fraudulent nature…
You can read the article in its entirety, on the official website of https://blog.eduonix.com/software-development/neural-network-python/
Thanks a lot … I hope your thoughts, your geniuses can have as much resonance as possible.