shoemai

50k to 100k median doesn't seem too far fetched to me. Myself and many people I know were in that range.

I think this is a popular opinion that's just not realistic. There is a >0% of the population (much greater IMO) that is more productive learning in a structured environment. For those people there is an amount >0$ that they would be willing to pay for that structured environment. To say otherwise is basically just a glib, "Well I did it so why can't you?"

I feel like it's very easy to fudge the numbers just due to all the different factors involved. Would student reviews be a better way to evaluate bootcamps? I know I've seen some bootcamps on yelp with reviews. This seems like it would be better than a self-reported statistic.

> the issue is that it is both very difficult to measure, track and maintain data and it varies wildly.

Also went to a bootcamp and I agree with this. I think the variance in ability between students is greater than the variance between bootcamps. People come in with different levels of experience and backgrounds. People are looking for work in different places.

I think reviews from former students are a better solution than just numbers.

Do you find that every student wants to be a full time developer though? What if the student were fully capable of finding a job as a developer but that's not what they wanted to do? Or maybe they were just open to many possibilities. Seems like just have a simple statistic like % of students that became devs would be 1. misleading and 2. acting against the bootcamp's own interests.

It's odd that nobody that's responded to you so far has pointed out that your comment is totally irrelevant to the comment you're responding to. Nowhere does GP imply that OP implies that you should render every state change server-side. The whole point of react is to render client-side.

If I'm on reddit and had to reload the page for every upvote/downvote, comment expand/collapse, show more comments, and reply the ux would be significantly worse. And thats not even a site that has that much user interaction.

Basically each input gets multiplied by some weight that gets adjusted through each iteration. The product of the input and weight gets put through an activation function, and the outcome of that can be interpreted as the network's prediction of the class.

So you see the first neuron's input is just x1. You can see in the little graph at x1 that it's split down the middle with orange on one side and blue on the other. You can think of adjusting the weight on that neuron as adjusting where along the x axis the split occurs. All points on the orange side are classified orange and all on the blue side are classified blue. If you picked a data set like the spiral one or whatever, that neuron alone isn't going to make very many correct classifications. That's because it only gets the x1 value as input and can only affect the output by multiplying x1 by some weight, which would only have the affect of shifting the classification boundary left or right. You can see the same thing happening for the second neuron with input x2 except that now it splits along the y axis. Again that alone isn't going to match the data very well.

But then you get to the second layer, and the input of each neuron in the second layer is the output of each neuron in the first layer. So these neurons are able to take into consideration both x1 and x2 and are able to divide the data in more complex ways. So you can think of the neurons in each layer of the neural network as being able to consider more and more complex properties of the data in forming its output.

Super basic explanation:

It's training a neural network to classify a data set with two classes (orange or blue) and the data has two features (x1 or x2). All the orange and blue dots are the training data. So if you take a dot on the graph with coordinates (-2, 4) and it's blue, that would mean that a data point with x1 = -2 and x2 = 4 has the class blue.

You can think of a neural network as a function that can take in arbitrary features (in this case x1 and x2) and tries to output the correct class. That's what the orange and blue colors in the background are, the neural network's guess at the correct classification for any given point (x1, x2).

When you hit play, it iterates through the training data making adjustments to each neuron in the network so that it gets closer to predicting the right class.

If you want to see how well the neural network performs on data it wasn't trained on, you can click "show test data".