What is overfitting and how can you prevent it in your ANN training process?

Overfitting in an Artificial Neural Network (ANN) is like teaching a dog to fetch a ball but only using one specific ball, the dog struggles with new balls to prevent overfitting, use diverse training examples, simplify the model, apply regularization, use dropout (randomly ignoring some neurons during training), perform cross-validation (testing with different data subsets), and use early stopping (stop training when improvement stops). These methods help the ANN generalize better to new, unseen data.

Consider data as follows: X, Y 1, 2 2, 4.3 3, 5.9 4, 7.8 Given data, we can fit a cubic polynomial (y = ax3 + bx2 + cx + d). But if we look closely and believe that data contains noise, we can fit a straight line (y = 2x + error) which is less complex. Hence, when we model the data too specifically, so much that we model the noise in it as well, we are "over" fitting the data. This leads to achieving high accuracy on training data but poor accuracy on validation data i.e., poor model generalization. Methods to prevent overfitting in ANN: 1. Batch Normalization 2. Dropout 3. Early-stopping training process 4. Adaptive LR optimizers and LR Schedulers 5. Reducing number of neurons/layers 6. Data Augmentation to increase training data

Overfitting makes your machine learning model a poor predictor for new data, as it gets too attuned to the training set. It's often due to overly complex models with too many parameters, insufficient data, or excessive training. To mitigate this, you can reduce your model's complexity or employ regularization, early stopping, and dropout techniques. Additionally, cross-validation can offer insights into how well the model generalizes to new data. Please keep it simple and be careful about how well the model is learning the broader aspects of your data, not just memorizing the details.

Overfitting occurs when an artificial neural network (ANN) model excels at predicting outcomes on the training data but struggles when applied to test data or new data. This happens because the model has essentially memorized the intricate details and noise present in the training data, rather than grasping the broader patterns and trends inherent to the problem. Consequently, overfitting hampers a model's accuracy and its ability to generalize, potentially resulting in misleading conclusions and suboptimal decision-making.

A way to think about overfitting is that the system is studying by heart the examples that it is given and not the characteristics of the data. the system is large enough to simply memorize the training data instead of generalizing according to it the result is that the accuracy on the training data is amazing but very poor on previously unseen data.

Overfitting in artificial neural network (ANN) models can be attributed to several factors. These include excessive model complexity due to too many parameters or hidden layers, which can lead to memorizing training data rather than capturing general patterns. Insufficient or noisy data can also contribute, as it limits the quality and diversity of information available for learning. Moreover, an excessive number of training epochs or iterations can cause the model to fit the training data too closely, possibly emphasizing errors and outliers excessively.

Common causes of overfitting are, 1. Inclusion of too many trivial, non important parameters as a part of the training data 2. Not enough number of training data points which prompts the model to derive parameter coefficients from limited data 3. Above problems prompt the training model to give excessive weights/coefficients to training data variables

Detecting overfitting in a model involves monitoring its performance on both the training and validation data. The validation data is a separate subset used for evaluation, distinct from the training set. Key metrics like accuracy, loss, or error are compared between these sets. A substantial gap or divergence between training and validation metrics indicates overfitting. For instance, if training accuracy is notably high while validation accuracy is considerably lower, it signifies that the model is overfitting

By enhancing the quantity and quality of data, we can stop it. Data augmentation can help in this case to add diversity to the datasets. We occasionally overcomplicate our networks even when it isn't necessary, thus simplyfying your network can also be a good option. Others include regularisation, drop out, and early stopping as ways to avoid overfitting.

To prevent overfitting, various techniques can be employed to either reduce model complexity and variance or enhance data quality and quantity. Regularization, for instance, introduces a penalty term based on weight or bias magnitudes in the model's cost function. Dropout randomly deactivates some units or connections in an ANN during training. Data augmentation involves creating new or altered data from existing data through transformations like rotation, scaling, cropping, flipping, or noise addition. These strategies help in building a more robust and diverse model, improving generalization, and mitigating the impact of noise or bias.

I have found it useful to monitor training and validation loss per epoch. Ideally, they approach zero together; divergence can indicate overfitting. Additionally, the EarlyStopping callback in Tensorflow can prevent overfitting by ending training early if the model is not improving after a defined number of epochs.

Its always advisable(wherever possible) to revalidate the results of the model on validation data by humans such that input/s of human validation can be fed back to the model, to retrain ANN's, in order to reduce overfitting.

To implement regularization, dropout, or data augmentation techniques in your artificial neural network (ANN) training process using TensorFlow and Keras, you can follow these guidelines: Regularization: Use the kernel_regularizer or bias_regularizer arguments in the Dense or Conv2D layers. Specify the type and amount of regularization using options like l1, l2, or l1_l2.