Learn more about computerscience with this collection
Understanding machine learning models
Improving data analysis and decision-making
How Google uses logic in machine learning
Validation tests are used to identify and tune the ML model.
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As AI integration across industries picks greater pace, ML engineers are confronted with a sad reality - once stakeholders identify a use case with proven ROI, they are eager to jump onto the AI ship, and dat...
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Testing data is used to test the validity of the training data set. Training data is not used for testing because it will produce the expected output. The testing data set comprises of 20 percent of the total data.
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The process of curating datasets for machine learning starts well before availing datasets. Here’s what we suggest:
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If you have a small dataset, using a model pre-trained on large datasets can be a good idea. You can use your small dataset to fine-tune it.
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Data is the new oil - and just as oil needs the right refining to come into perfect usage, data too needs curing. The power of your machine learning models will greatly depend on the quality of your data.
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ML engineers depend on data during each step of their AI journey – from model selection, training, and tuning to testing. These datasets usually fall under three categories:
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The training data set is used to train an algorithm, apply concepts, learn, and give results. Around 60 percent of data is training data.
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To develop and manage a production-ready model, you must work through the following stages:
Train an ML model on your data:
Train model
Tune hyper...
Facebook, admitted they designed the program to monopolize our time and attention as much as possible. Specifically exploiting the principles of
We naturally seek validation from external sources like friends, family and social media (number of likes), but fail to realize that true happiness comes from within us, and real freedom is when we focus on ourselves, not others.
If we only strive for external vali...
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