Backpropagation and Overfitting in Machine Learning

Backpropagation

• Backpropagation is a supervised learning algorithm used or employed to train artificial neural networks.
• It involves the iterative adjustment of network weights based on the computed error between predicted and actual outputs.
• The goal is to minimize this error and improve the model's accuracy.

Backpropagation Algorithm

• Step 1: Input data X is received through the preconnected path.
• Step 2: The input is processed using initial weights W, typically chosen randomly.
• Step 3: Compute the output of each neuron, moving from the input layer to the hidden layer and finally to the output layer.
• Step 4: Determine the error in the outputs using the formula:
• Backpropagation Error = Actual Output – Desired Output.
• Step 5: Starting from the output layer, backtrack to the hidden layer, adjusting the weights to minimize the error.
• Step 6: Repeat the entire process until the desired output is reached.

Variants of Backpropagation

Static Backpropagation

• Static backpropagation refers to a network designed to map static inputs to static outputs.
• These networks are capable of addressing static classification problems, like optical character recognition (OCR).
• Static Backpropagation is an extension of the traditional backpropagation algorithm specifically,
• designed for static neural networks, where the input and output dimensions remain constant.
• It's commonly used in feedforward neural networks for tasks like image classification or pattern recognition.

Recurrent Backpropagation

• Recurrent Backpropagation is a modification of the traditional backpropagation algorithm tailored for recurrent neural networks (RNNs).
• Recurrent Neural Networks (RNNs) are specifically crafted to handle sequences of data.
• The recurrent backpropagation network is employed for fixed-point learning.
• This means that during neural network training process, the weights are numerical values
• that determine how much nodes or neurons influence output values.
• Example: Consider an RNN tasked with predicting the next word in a sentence.

Types of Models in Machine Learning

Underfit Model

Overfit Model

Good Fit Model

What is Overfitting in Machine Learning?

• Overfitting occurs when a model becomes too proficient at learning the training data.
• While this might seem beneficial, it's actually a drawback.
• When a model is overly trained on the training data,
• it performs worst on the test data or any new data provided.
• Technically, the model learns the details as well as the noise or inconsistency of the train data.
• This is why we say the model's performance is not up to par.

Techniques to Avoid Overfitting

• Cross-Validation: Use techniques like K-fold cross-validation to assess model performance on multiple subsets of the data, ensuring it generalizes well.
• Resampling: Techniques like bootstrapping help in creating multiple datasets, reducing the impact of outliers and noise.
• Feature Reduction: Limit the number of features to prevent the model from memorizing noise in the training data.

Role of Regularization

• Regularization is a technique that adds penalties to the model's coefficients, discouraging them from becoming overly large.
• By penalizing coefficients, regularization helps prevent the model from fitting the noise and details present in the training data.

L1 Norm (LASSO) and L2 Norm (Ridge)

• L1 Norm (LASSO): Adds the sum of absolute values of coefficients to the cost function.
• Effect: Encourages sparsity in coefficients, effectively eliminating less impactful features.

• L2 Norm (Ridge): Adds the sum of squared values of coefficients to the cost function.
• Effect: Discourages overly large coefficients, preventing extreme weights on specific features.

Practical Implementation

• Coefficient Penalization: Regularization adds penalty terms to the cost function,
• making the model optimize coefficients considering both accuracy and simplicity.
• Optimizing Coefficients: The model aims to minimize the cost function,
• striking a balance between fitting the training data well and avoiding excessive complexity.

Assessment Through RMSE

• A reliable model displays a comparable Root Mean Squared Error (RMSE) for both the training and test sets.
• If there's a significant difference, it suggests the model is overfitting to the training set.

Applications of Neural Networks

• Neural networks find applications across various domains due to their ability to learn from data and make predictions or decisions.
• Here are some key applications:

Image and Pattern Recognition

Facial Recognition

Object Detection

Handwriting Recognition

Natural Language Processing (NLP)

Sentiment Analysis

Language Translation

Speech Recognition:

Healthcare

Disease Diagnosis

Drug Discovery

Finance

Credit Scoring

Algorithmic Trading

Conclusion

• Backpropagation enables effective training of neural networks by iteratively adjusting weights to minimize prediction error.
• Overfitting occurs when a model over learns the training data, performing well on training data but poorly on new data.