And no, it’s not in Python. Or C++.
It might not even be in a language you already know, based on how relatively unpopular it is. But if you’ve studied math or physics at university you’ve probably at least heard of it.
I’m talking about Mathematica. And since version 11, it’s been possible to train neural networks directly in Mathematica.
Yes. Seriously. Keep reading.
If you’ve ever been interested in symbolic math, let’s get it out there: there’s really no parallel. Matlab just has basic symbolic capabilities, and is not really at all comparable (although both Mathematica and Matlab share…
The awesomeness of CI is that docs and tests run automatically. Requiring that tests pass on merges is probably the most critical CI feature to large projects.
The pain of CI for Swift is that for simple things like generating documentation, we have to build the entire package/app, which usually makes our pipelines take forever.
If docs aren’t the most critical part your design, you can avoid using CI and push the docs manually to host your site. Here we will do it using jazzy.
Click on the method or class to document, then use
command-option-/ to insert a docstring…
How to redefine everything from numpy, and some actually useful tricks for part assignment, saving custom layers, etc.
We’re talking about creating custom TensorFlow layers and models in:
No wasting time with introductions, let’s go:
tf.map_fn, e.g. converting vectors of size
nin a batch
(batch_size x n)to diagonal matrices of size
(batch_size x n x n):
mats = tf.map_fn(lambda vec: tf.linalg.tensor_diag(vec), vecs)
How to serialize nested layers in TF
A custom TF layer is one that subclasses from
tf.keras.layers.Layer . This is powerful on its own, but a particularly desirable feature is to have nested layers. Serializing nested layers is a little bit of a headache, however, but necessary in order to save models with nested layers using
Let’s first make a custom layer:
This includes the
from_config methods which are used to serialize the custom layer. Custom attributes like
self.x are included by first calling the super class’s
get_config() , and then using
Without amplifying the noise!
That’s the real trick — how to differentiate a noisy signal, without amplifying the noise. It comes up all the time:
The well known problem for a noisy signal is that:
If you think you’re clever, you’ll come up with some sort of smoothing…
A tutorial on probabilistic PCA.
There’s hardly a data scientist, scientist, programmer, or even marketing director who doesn’t about PCA (principal component analysis). It’s one of the most powerful tools for dimensionality reduction. If that marketing director is collecting survey data and looking to find target consumer groups for segmentation and analyzing the competition, PCA may very well be in play.
But you may have missed one of the simplest generative models that comes from an alternate view on PCA. If we derive PCA from a graphical model perspective, we arrive at probabilistic PCA. It allows us to:
Open source iOS app for referencing face mask data
Masks are important. Choosing the right type of mask and understanding its qualifications is critical. Currently, mask information is scattered between the openFDA database, FDA websites and CDC websites.
“The Masked Manual” is a free iOS app that shows mask information compiled from openFDA, FDA websites and CDC websites. It lets you easily search for masks and see their qualifications, as well as instructions for wearing it. You can also use the camera to recognize and find your mask by scanning its packaging.
Your state’s voting power in the electoral college.
With the recent 2020 election in the United States coming to a close (or should I say closed?), the arguments about the electoral college are flaring up again. Every four years, citizens in dense population areas argue in favor of either changing or abolishing the electoral college system, citing that their votes are being discounted relative to more rural areas.
What really is your voting power in different states? I made a little Flask application that you can find hosted on Heroku here:
And where are they in machine learning?
We will review the theory for line search methods in optimization, and end with a practical implementation.
In all optimization problems, we are ultimately interested in using a computer to find the parameters
x that minimize some function
-f(x) , if it is a maximization problem). Starting from an initial starting guess
x_0, it is common to proceed in one of three ways: