| // stub for typescript | |
| declare function BigInt(n: number): number | |
| abstract class Trampoline<A> { | |
| abstract map<B>(ab: (a: A) => B): Trampoline<B> | |
| abstract flatMap<B>(ab: (a: A) => Trampoline<B>): Trampoline<B> | |
| zip<B>(tb: Trampoline<B>): Trampoline<[A, B]> { | |
| const ta = this | |
| return ta.flatMap(a => tb.map(b => [a, b] as [A, B])) |
| # MikroTik (RouterOS) script for automatically setting DNS records | |
| # for clients when they obtain a DHCP lease. | |
| # | |
| # author SmartFinn <https://gist.github.com/SmartFinn> | |
| # based on https://github.com/karrots/ROS-DDNS | |
| :local domain; | |
| :local fqdn; | |
| :local hostname; | |
| :local safeHostname; |
You may have thought of running nightmare on AWS Lambda. But before we can run it on Lambda, we need first to make it run on Amazon Linux.
According to AWS Documentation on Lambda Execution Environment and available Libraries we would need this AMI image with this alias amzn-ami-hvm-2016.03.3.x86_64-gp2. Keep in mind that AMI-image-id for this instance would be different in different regions (eg):
eu-west-1 - ami-f9dd458aus-east-1 - ami-6869aa05
You may have thought of running nightmare on AWS Lambda. But before we can run it on Lambda, we need first to make it run on Amazon Linux.
According to AWS Documentation on Lambda Execution Environment and available Libraries we would need this AMI image with this alias amzn-ami-hvm-2016.03.3.x86_64-gp2. Keep in mind that AMI-image-id for this instance would be different in different regions (eg):
eu-west-1 - ami-f9dd458aus-east-1 - ami-6869aa05
| '''This script goes along the blog post | |
| "Building powerful image classification models using very little data" | |
| from blog.keras.io. | |
| It uses data that can be downloaded at: | |
| https://www.kaggle.com/c/dogs-vs-cats/data | |
| In our setup, we: | |
| - created a data/ folder | |
| - created train/ and validation/ subfolders inside data/ | |
| - created cats/ and dogs/ subfolders inside train/ and validation/ | |
| - put the cat pictures index 0-999 in data/train/cats |
| '''This script goes along the blog post | |
| "Building powerful image classification models using very little data" | |
| from blog.keras.io. | |
| It uses data that can be downloaded at: | |
| https://www.kaggle.com/c/dogs-vs-cats/data | |
| In our setup, we: | |
| - created a data/ folder | |
| - created train/ and validation/ subfolders inside data/ | |
| - created cats/ and dogs/ subfolders inside train/ and validation/ | |
| - put the cat pictures index 0-999 in data/train/cats |