Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.
Avoid being a link dump. Try to provide only valuable well tuned information.
Neural network links before starting with transformers.
| # Stop all containers | |
| docker stop `docker ps -qa` | |
| # Remove all containers | |
| docker rm `docker ps -qa` | |
| # Remove all images | |
| docker rmi -f `docker images -qa ` | |
| # Remove all volumes |
Typing vagrant from the command line will display a list of all available commands.
Be sure that you are in the same directory as the Vagrantfile when running these commands!
vagrant init -- Initialize Vagrant with a Vagrantfile and ./.vagrant directory, using no specified base image. Before you can do vagrant up, you'll need to specify a base image in the Vagrantfile.vagrant init <boxpath> -- Initialize Vagrant with a specific box. To find a box, go to the public Vagrant box catalog. When you find one you like, just replace it's name with boxpath. For example, vagrant init ubuntu/trusty64.vagrant up -- starts vagrant environment (also provisions only on the FIRST vagrant up)(by @andrestaltz)
If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.
| int xPos = (canvas.getWidth() / 2); | |
| int yPos = (int) ((canvas.getHeight() / 2) - ((textPaint.descent() + textPaint.ascent()) / 2)) ; | |
| //((textPaint.descent() + textPaint.ascent()) / 2) is the distance from the baseline to the center. | |
| canvas.drawText("Hello", xPos, yPos, textPaint); |
| Using gem aws-sdk for a ror application for uploading images to s3 | |
| Uploading images to a fixed bucket with different folders for each object or application. | |
| The s3 keeps a limitation on the number of buckets creattion whereas there is no | |
| limitation for content inside a bucket. | |
| This code will upload image for a user to s3 using aws-sdk gem. The bucket and the image uploaded are made public | |
| so that the images uploaded are directly accessible. The input it takes is the image complete path | |
| where it is present, folder in which it should be uploaded and user_id for whom it should | |
| be uploaded. |
| // SymSpell: 1000x faster through Symmetric Delete spelling correction algorithm | |
| // | |
| // The Symmetric Delete spelling correction algorithm reduces the complexity of edit candidate generation and dictionary lookup | |
| // for a given Damerau-Levenshtein distance. It is three orders of magnitude faster and language independent. | |
| // Opposite to other algorithms only deletes are required, no transposes + replaces + inserts. | |
| // Transposes + replaces + inserts of the input term are transformed into deletes of the dictionary term. | |
| // Replaces and inserts are expensive and language dependent: e.g. Chinese has 70,000 Unicode Han characters! | |
| // | |
| // Copyright (C) 2012 Wolf Garbe, FAROO Limited | |
| // Version: 1.6 |