Many online courses are useless. They will not get you anywhere. But there are gems out there. Here is a curated list that will help you build a machine learning career without paying a fortune in tuition fees. ↓
In theory, you can model any function using a neural network with a single hidden layer. However, deep networks are much more efficient than shallow ones. Can you explain why?
Basically, out of 2,097 positive results, 99 of them are genuinely sick while everyone else is not. The probability that I have the disease is 99 / 2,097 ≈ 4.72%. So, I'm probably not sick.
My test came back positive, so I belong to one of these two groups: • The 99 people that are genuinely sick. • The 1,998 people that are healthy but were diagnosed (incorrectly) as sick.
Now, let's look at the group of the 99,900 healthy patients: • 97,902 of them will be diagnosed (correctly) as healthy. (98%) • 1,998 of them will be diagnosed (incorrectly) as sick. (2%)
First, let's look at the group of the 100 sick patients: • 99 of them will be diagnosed (correctly) as sick (99%) • 1 of them is going to be diagnosed (incorrectly) as healthy (1%)
Here is what we currently know: • Out of 1,000 people, 1 is sick • Out of 100 sick people, 99 test positive • Out of 100 healthy people, 98 test negative Let's assume the doctor tested 100,000 people (including me): • 100 of them are sick • 99,900 of them are healthy
To answer this question, we need to understand why the doctor tested me in the first place. If I had symptoms or if she suspected I had the disease for any reason, the analysis would be different. But let's assume that the doctor tested 10,000 patients for no specific reason.
I need your help. The doctor tested me, and I came back positive for a disease that infects 1 of every 1,000 people. The test comes back positive 99% of the time if the person has the disease. About 2% of uninfected patients also come back positive. Do I have the disease?
What is this thing? On Friday the 24th, you'll join a live space with another 49 people and me. You'll get to listen to me rambling about how you can build a career in Machine Learning. You will be able to ask questions. Some of that might be helpful and worth $2.99.
I just found out that this is only supported in iOS. To buy a ticket ($2.99) you need to be on iOS.
This is my first ticketed space. Only 50 people will participate, so I can make sure everyone gets their money's worth. I don't think tickets will be on sale for too long, so don't wait if you want to participate.
I've heard a couple of times that you shouldn't use Flask in production systems. This is not correct. Flask comes with a built-in web server that's not suitable for production. The solution is not to throw Flask away but to pair it with a production-ready WSGI server.
I'm not a mathematician. This thread aims to illustrate probabilities on an infinite space of outcomes using an informal explanation. For those looking for a more formal approach, I'd recommend reading about probability density and how it's used in a continuous context.
Whenever we are looking at an infinite space of outcomes, we say the following: "An event with zero probability implies that the event will almost never happen." This is accurate. Saying that the event will never happen (or is impossible) is not.