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Some machine learning libraries implement a training strategy that can be done by specifying the hyper-parameters (such as model, algorithm, optimizer, etc.) and also the data set properties. This gives the library the flexibility to choose the hyper-parameter that gives the best performance on the data set. Such training strategies are often called intelligent or active learning.
Setup: The setup of machine learning is that you have to first understand the data that you want to train the machine on. This requires collecting the data sets. Once you have collected the data with a representative amount of data, then you have to segregate it into the data training set and the data testing set.
Split: Once you have your data split into training and testing sets, you will then have to explore the data and you will want to find the features that you can take as input for to train the model. This is necessary to tell the machine how to discriminate between the data in training and testing.
Preparing Training and Testing Data: Once you have set up your data and features, then you will need to prepare the data before training. This includes standardizing data (e.g., reshaping data into 1-by-n arrays for use with linear models) and normalizing data (shifting and scaling data as appropriate to use with neural networks). You will also want to establish stopping criteria and other metrics for determining when you have sufficiently converged.
Training often requires for training data and validation data, in addition to effective and efficient tests of model performance. Training data can be used to assess how well the model's predictions and probabilities correlate with the truth values. Validation data can be used to determine when to stop training once the maximum accuracy has been achieved. d2c66b5586
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