Training a connectionist AI requires careful attention to the data that will teach it. Neural networks learn from examples, so the dataset we use for training has to meet certain criteria to ensure the model learns effectively. Below are the key aspects of training data.
https://infiniticube.com/blog/neural-network-big-data-creating-smart-systems-that-learn/#:~:text=Neural networks rely on extensive,a wider range of inputs
https://www.geeksforgeeks.org/deep-learning/why-deep-learning-is-important/
https://www.akkio.com/post/how-much-data-is-required-to-train-ml#:~:text=Quality and Noise within Data
https://developers.google.com/machine-learning/crash-course/overfitting/imbalanced-datasets#:~:text=Class
https://moldstud.com/articles/p-avoid-these-10-mistakes-in-nlp-deep-learning-models#:~:text=Additionally%2C maintaining a balanced dataset,and lead to biased predictions
https://www.ibm.com/think/topics/data-bias#:~:text=Data bias occurs when biases,models adversely affect model behavior
https://jetruby.com/blog/understanding-ai-training-data/#:~:text=In short%2C AI training needs,term efficiency