Gathering the raw digital ingredients
Washing and prepping the data
Slow-cooking in the neural oven
Taste-testing and seasoning
YOUR GOAL
Master of The digital cookbook for hungry algorithms
007skaty Safety filters remove toxic content from artificial intelligence training data.
007skaty Like washing muddy potatoes, data engineers remove duplicate sentences, broken HTML, and spam to protect AI from consuming bad data.
007skaty Feed your bot the right word ingredients to learn how AI processes human language.
Token chewing: Large language models do not read letters; they process numbers representing chunks of words called tokens.
The process of detecting and correcting or removing corrupt or inaccurate records from a database. MIT researchers in 2017 applied this process to filter over one million recipes for the Recipe1M+ dataset.
Cleaning raw datasets by removing HTML tags and spam can reduce an AI model's training time by up to what percentage?
007skaty Mixing grams and ounces ruins recipes, but standardizing measurements through data normalization ensures all variables have an equal say.
What is the process of finding and removing identical or highly similar text blocks from training data called?
What tool do data engineers use to automatically identify and filter out toxic language from raw text files?
What unit of data storage, equal to one million gigabytes, represents the scale of massive web datasets being filtered?
Which popular tokenization algorithm breaks words down into common sub-word units for modern language models?
In natural language processing, what is the basic unit of text that an AI model reads and predicts called?
What term is used in machine learning to describe the highly accurate, human-verified labels used as training targets?
In what year did Amazon launch its Mechanical Turk crowdsourcing platform, which became a vital tool for data labeling?