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In machine learning, an "epoch" refers to one complete pass through the entire training dataset during the training of a neural network or another machine learning model. During each epoch, the model processes each example in the training dataset once, updating its internal parameters (weights and biases) based on the errors it makes when making predictions on the training data.

Here's how the training process typically works over multiple epochs:

1. Initialization: At the start of training, the model's parameters are initialized randomly or with some predefined values.

2. Forward and Backward Pass: During each epoch, for each example in the training dataset, the model computes a prediction (forward pass) and then calculates the error between the predicted output and the actual target output. It then uses a loss function to quantify this error.

3. Gradient Descent: After processing all examples in the training dataset for a given epoch, the model uses an optimization algorithm (e.g., gradient descent) to update its parameters in a way that reduces the error or loss function. The direction and magnitude of these parameter updates are determined by the gradients of the loss function with respect to the model's parameters.

4. Repeat: Steps 2 and 3 are repeated for a predefined number of epochs or until a stopping criterion is met (e.g., the loss converges to a satisfactory level or training time reaches a limit).

The choice of the number of epochs is a hyperparameter that machine learning practitioners need to tune. Too few epochs may result in an underfit model that doesn't capture the underlying patterns in the data, while too many epochs can lead to overfitting, where the model learns the noise in the training data and performs poorly on unseen data.

Monitoring metrics like the training loss and validation loss over epochs is crucial to determine when the model has learned as much as it can from the training data without overfitting. Techniques like early stopping can be employed to stop training when the validation loss starts increasing, indicating the onset of overfitting.

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To plot the loss (MSE) versus epoch for a machine learning training process, we typically need training data and a model to train. Here's a Python script using the popular deep learning library TensorFlow and its Keras API to demonstrate how to create such a plot for a simple linear regression model. Code:
         
       Output:    
         

In this script:

• We generate example data for a simple linear regression problem.
• We define a simple linear regression model using TensorFlow/Keras with one input and one output unit (for linear regression).
• We compile the model using Mean Squared Error (MSE) as the loss function.
• We train the model on the data for a specified number of epochs (training iterations) and collect the loss values for each epoch.
• Finally, we create a plot that shows how the loss (MSE) changes with each epoch during training.

Table 3955. Application of Epoch concept.

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