21–30 of 49 posts
>>> You are one buzzword away from being a professional. Instead of fitting a line to just eight datapoints, we will now fit a line to 8-million datapoints. Welcome to big data. LOL :) (Side-note: 8 million is still not big data)
lol
>>> You are one buzzword away from being a professional. Instead of fitting a line to just eight datapoints, we will now fit a line to 8-million datapoints. Welcome to big data. LOL :) (Side-note: 8 million is still not big data)
Big Data is a reference to complexity of the data & underlying system that data represents, NOT the number of datapoints. lol
Simple example? Before finishing the first paragraph, it says "The slope and y-intercept of the line are determined using gradient descent." What on earth does that mean? Maybe they should teach mathematics in english at universities outside of english speaking countries. German mathematics does not help here. I wish there was a 4GL like SQL for machine learning using dynamic programming for algorithm selection and m…
and it is absolutely a good idea, as long as you include validation and QA abilities right along side train/predict.
I like these kind of "Hello, world!" examples for TensorFlow. As a TensorFlow beginner, I need all the references I can get. Here is what I need right now: "Hello, we meet again!". I can build a neural net model, and train (albeit, often badly) a model, but saving and restoring the trained weights so that I can run the model again is giving me fits. I am clearly missing something fundamental about how to restore a Te…
I think that having to use a duck typed language makes it so much harder than it has to be. The API is huge, and you get really no help from the IDE. If I could do tf.train.GradientDescentOptimizer as in the example, and then get autocomplete to what I can do next with that object it would really help. Or list functions that takes that kind of object as input. Now, one is searching in the dark.
I'm guessing english is not your first language, so I just wanted to point out that "bare bottom" is generally synonymous with "uncovered buttocks" ie. in the context of changing an infant's diaper. Perhaps you were meaning to put "bare bones"? Google's definition of the latter is "reduced to or comprising only the basic or essential elements of something." Don't want to detract from your point but I think your title…
That's nifty; I was looking for something like that just a few weeks ago for a work demonstration! Ended up doing https://gist.github.com/fuglede/ad04ce38e80887ddcbeb6b81e97b... instead.
Earlier quoted context omitted.
As a person that uses TensorFlow for his day job: I find that saving and restoring are of the weirder things with TensorFlow, you can either go all out an decide to save out all the variables, or only the ones needed for the model. You usually don't want to save out gradients (which are also variables) since they take up a bunch of space and aren't actually that useful to restore. Now on the other, what are model var…
+1
I get the output of the model (y_model = m*xs[i]+b), it's the y = mx + b where we know x (from the dataset) and have y be a variable.
The error is where I start to lose it, so I get the idea of the first part (ys[i]-y_model). It's basically the difference between the actual y value (from the dataset). I get that we want this number to be as small as possible as the closer to zero it is for the entire dataset that means we get closer to the line going through (or near) all the points and the closest fit will be when this total_error is nearest to zero.
What I don't get is the squaring of the difference. Is it just to make the difference a larger number so that it's a little more normalized? How do you get to the conclusion that it needs to be normalized? Same thing with the learning rate? I believe these to be correlated but I can't tell you how...