Whether you’re just getting started or have been working with AI models for a while, there are some common machine learning and deep learning mistakes we all need to be aware of and reminded of from time to time. These can cause major headaches down the road if left unchecked! If we pay close attention to our data, model infrastructure, and verify our outputs as well we can sharpen our skills in practicing good data scientist habits.
Machine Learning and Deep Learning Data Mistakes to Avoid
When getting started in machine learning and deep learning there are mistakes that are easy to avoid. Paying close attention to the data we input (as well as the output data) is crucial to our deep learning and neural network models. The importance in preparing your dataset before running the models is imperative to a strong model. When training an AI model, 80% of the work is data preparation (gathering, cleaning, and preprocessing the data), while the last 20% is reserved for model selection, training, tuning, and evaluation. Here are some common mistakes and limitations we face when training data-driven AI models.