$ xvfb-run -s "-screen 0 1400x900x24" jupyter notebook
import matplotlib.pyplot as plt
%matplotlib inline
def show_state(env, step=0):
module Entry::TrackerBlocking | |
extend ActiveSupport::Concern | |
included do | |
has_many :blocked_trackers | |
end | |
email_service_blockers = { | |
"ActiveCampaign" => /lt\.php(.*)?l\=open/, | |
"AWeber" => "openrate.aweber.com", |
import torch | |
import torch.nn.functional as F | |
def maml_grad(model, inputs, outputs, lr, batch=1): | |
""" | |
Update a model's gradient using MAML. | |
The gradient will point in the direction that | |
improves the total loss across all inner-loop |
In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.
At the top of the file there should be a short introduction and/ or overview that explains what the project is. This description should match descriptions added for package managers (Gemspec, package.json, etc.)
Show what the library does as concisely as possible, developers should be able to figure out how your project solves their problem by looking at the code example. Make sure the API you are showing off is obvious, and that your code is short and concise.