tldr; Not recommended for non-technical people. Not recommended as a primary machine. But if you want a small secondary laptop for travel and light work, and if you install Ubuntu on it, this laptop is a surprising treat!
If installing a new operating system terrifies you (it’s actually not that hard), buy something else. If it does not, then this is a great little machine. I find myself using this little HP Stream more than my other powerful laptop. The utility of a physically light laptop is not to be underestimated.
Note that some reviews claimed that if you remove all the bloatware of it, this machine runs Windows fine. So you might get a decent Windows experience if you remove bloatware at the start.
Now what is this HP Stream 11 you might ask. Well it’s …
A light travel laptop
This machine is light in all the sense of the words. Physically, it’s a light machine; it’s tiny. Color-wise, it’s a light bright blue or purple. Spec-wise, it’s very light.
They should have called this HP Light 11.
But light can be good. Sometimes I want a light machine, one that I don’t care if it gets lost or stolen, or dropped and broken. With a light machine, I can fit it into a travel bag, and do some rough prototyping before pushing the code to more capable machines.
Continue reading “HP Stream 11 review – running Ubuntu 16”
On a Ubuntu laptop, with a NVIDIA GPU, when trying to open Mendeley, you get this rather unhelpful error:
The application Mendeley Desktop has closed unexpectedly.
I’m sure there are many causes for this error, but one unexpected reason you might get this error is related to your graphics card.
If you have a NVIDIA GPU on your laptop, try to switch to your Intel graphics card instead of NVIDIA..
To switch to your Intel graphics card, open your terminal and type:
sudo prime-select intel
Then restart Mendeley. Like magic and deep learning, it just seems to work.
(if you need to switch back to your NVIDIA card, just type
sudo prime-select nvidia)
Scenario: You’re trying to get your GPU to work in TensorFlow on a Ubuntu Laptop. You’ve already installed Tensorflow, Cuda, and Nvidia drivers.
You run python and import TensorFlow:
import tensorflow as tf
And you see encouraging messages like:
"successfully opened CUDA library libcublas.so locally"
But in Python, when you run,
You get this cryptic error:
failed call to cuInit: CUDA_ERROR_UNKNOWN
Here’s how to fix this.
Continue reading “TensorFlow – failed call to cuInit: CUDA_ERROR_UNKNOWN”
There are so many ways to normalize vectors… A common preprocessing step in machine learning is to normalize a vector before passing the vector into some machine learning algorithm e.g., before training a support vector machine (SVM).
One way to normalize the vector is to apply
l2-normalization to scale the vector to have a
unit norm. “Unit norm” essentially means that if we squared each element in the vector, and summed them, it would equal
(note this normalization is also often referred to as,
unit norm or a
vector of length 1 or a
So given a matrix
X, where the
rows represent samples and the
columns represent features of the sample, you can apply
l2-normalization to normalize each row to a unit norm. This can be done easily in Python using sklearn.
Here’s how to l2-normalize vectors to a unit vector in Python
import numpy as np
from sklearn import preprocessing
# Two samples, with 3 dimensions.
# The 2 rows indicate 2 samples,
# and the 3 columns indicate 3 features for each sample.
X = np.asarray([[-1,0,1],
[0,1,2]], dtype=np.float) # Float is needed.
# [[-1. 0. 1.]
# [ 0. 1. 2.]]
# l2-normalize the samples (rows).
X_normalized = preprocessing.normalize(X, norm='l2')
# After normalization.
# [[-0.70710678 0. 0.70710678]
# [ 0. 0.4472136 0.89442719]]
Now what did this do?
Continue reading “How to normalize vectors to unit norm in Python”
Ah the world wide web… the old www. So many factors to consider when developing web applications, especially if you’re used to developing in a relatively simple research environment.
One thing you will think about is how to get Google to include your page in it’s searchers (i.e., get indexed). To do this, we’ll submit a
sitemap to Google. Basically a sitemap lists the links that you want Google to start indexing (so other people can find it when they search the web).
If you’re using WordPress, this is pretty simple, and you can just download a plugin and skip to the last step below (step 6).
However, if you’re using Google App Engine to run your site, then you need to do a few more steps. So here’s instructions for how to do this.
Continue reading “How to upload sitemap.xml to google app engine”
I’m running Ubuntu on my Lenovo Y50 laptop, with a Nvidia GPU. And every time I do an update (or restart it?), I see the Ubuntu logo, hear the chime to log in, and then see a blank black screen, or a small white dot in the upper corner.
Other times, after a reboot, I get to the login screen, enter my username and password, then everything flickers violently, and it loops back to asks me to enter in my info again.
Today this post is not about how to permanently fix this (although that would be nice), but rather how to get your GUI back (until you update/restart your machine again).
It seems that on some laptops, the Nvidia drivers and Ubuntu do not always nicely play together. Why? I am not sure.
But anyways, here’s how to get fix your laptop when Ubuntu has a black screen on login (assuming your problem is related to the Nvidia drivers).
Continue reading “ubuntu – black screen on ubuntu laptop after installing nvidia drivers”
– you are using the Google App Engine (GAE) development server with Python
– you installed the Anaconda Python distribution
– you want to use the Numpy library with GAE
On Ubuntu and on Mac (but not Windows for some reason), you get this error when trying to deploy:
google app engine ImportError: No module named _ctypes
The tldr; solution
Create an Anaconda environment using numpy 1.6 and python 2.7:
conda create -n np16py27 anaconda numpy=1.6 python=2.7
Load this specific environment from the command line:
Run your GAE dev server:
That’s it! You can read more details below if you are interested.
Continue reading “Using numpy on google app engine with the anaconda python distribution”
Here’s how to compute true positives, false positives, true negatives, and false negatives in Python using the Numpy library.
Note that we are assuming a binary classification problem here. That is a value of
1 indicates a positive class, and a value of
0 indicates a negative class. For multi-class problems, this doesn’t really hold.
Continue reading “how to compute true/false positives and true/false negatives in python for binary classification problems”
Here’s how to debug your code when using a Jupyter/iPython notebook.
Tracer()(). Here’s an example using a simple function (based on this lucid explanation).
x = 10
# One-liner to start the debugger here.
from IPython.core.debugger import Tracer; Tracer()()
x = x + y
for i in range(10):
x = x+i
When the debugger reaches the
Tracer()() line, a small line to type in commands will appear under your cell.
Simply type in the variable names to check the values or run other commands. Below I’ve listed some practical Python PBD commands. More can be found here.
Continue reading “How to debug a Jupyter/iPython notebook”
– Google app engine with Python on Ubuntu
– Running the local development server.
If you start getting this error:
"importerror: no module named webapp2"
$ dev_appserver.py helloworld/
You might want to go back to an earlier version for local development.
After downloading “
I got errors with
Luckily, I had a previous version “google_appengine_1.9.34.zip”
So I deleted 1.9.37. And used 1.9.34 instead.
Maybe this will work for you.