DataSets
Data Preparation Process
The data preparation process in the mLearning module is closely related to the inputs and labels used by the neural networks and neurons.
During data preparation, the machine learning model in the mLearning module organizes the input data (features) and their corresponding target values (labels) into a format that can be used for training the neural network. The input data represents the information or features used by the neural network to make predictions, while the target values (labels) represent the ground truth or correct outputs that the network should learn to produce.
For example, in a supervised learning scenario, where the neural network is trained with labeled data, the data preparation process will involve organizing the training dataset into input-label pairs. Each input sample contains the features (input data), and each corresponding label represents the correct output value for that input.
Here's an example of how the data preparation might look for a simple supervised learning problem:
Training Dataset:
Input 1: [x1, x2, x3, ...] Label 1: y1 (correct output for Input 1)
Input 2: [x1, x2, x3, ...] Label 2: y2 (correct output for Input 2)
...
During the training process, the machine learning model feeds each input sample (features) to the neural network, and the network produces an output based on its current weights and biases. The model then compares the predicted output with the corresponding target value (label) using a loss function to measure the error.
The goal of training is to adjust the weights and biases of the neural network so that it can minimize the error between its predictions and the actual target values (labels). This adjustment is achieved through the backpropagation algorithm, which updates the neural network's parameters based on the gradients of the loss function with respect to the weights and biases.
In summary, data preparation in the mLearning module is crucial to ensure that the neural network receives the appropriate inputs and labels for training, and it plays a fundamental role in the process of optimizing the network's parameters to make accurate predictions on new data.
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Training Set and Test Set
In machine learning, both the training set and the test set consist of labeled data. The key difference isn't in how they're labeled but rather in how they're used within the machine learning process:
Training Set: This set is used to train the machine learning model. It consists of m_train images, (will use cats as an example) each labeled as a cat (y=1) or non-cat (y=0). The model learns to associate the features of the images (input) with these labels (output) through various learning algorithms. The labels for the training set are provided by humans (developers, data annotators, etc.) and are used to guide the model in learning the correct associations.
Test Set: After the model has been trained on the training set, it needs to be evaluated to see how well it has learned to classify new, unseen data. The test set serves this purpose. It consists of m_test images, each also labeled as cat (y=1) or non-cat (y=0). These labels are also provided by humans and are considered the ground truth for the test data. The difference is that the model does not learn from the test set; instead, it predicts the labels for these images, and its predictions are compared against the actual labels to evaluate the model's performance.
The reason for having these separate sets is to ensure that the model can generalize well to new, unseen data. If you used the training data to evaluate the model, you would not get a true sense of its performance because the model has already seen and learned from those examples. By testing the model on a separate set of data that it hasn't seen before (the test set), you can assess how well the model has learned to generalize from the training data to new, unseen examples.