getting started with neural networks summary you're now able to handle the most common kinds of machine-learning tasks on vector databinary classificationmulticlass classificationand scalar regression the "wrapping upsections earlier in the summarize the important points you've learned regarding these types of tasks you'll usually need to preprocess raw data before feeding it into neural network when your data has features with different rangesscale each feature independently as part of preprocessing as training progressesneural networks eventually begin to overfit and obtain worse results on never-before-seen data if you don' have much training datause small network with only one or two hidden layersto avoid severe overfitting if your data is divided into many categoriesyou may cause information bottlenecks if you make the intermediate layers too small regression uses different loss functions and different evaluation metrics than classification when you're working with little datak-fold validation can help reliably evaluate your model licensed to