Implementing Continual Learning in Machine Learning
Pre-requisites
pip install LogisticRegrssion
pip install numpy
Let’s see this use case wherein we need to categorize end result (apples and bananas) primarily based on their capabilities ( weight and color). We will simulate a persistent gaining knowledge of state of affairs in which new fruit statistics is continuously coming in, and our version have to adapt to these new facts points with out forgetting the preceding ones.
Python
from sklearn.linear_model import LogisticRegression import numpy as np # Initialize a logistic regression model clf = LogisticRegression() # Define initial training data X_initial = np.array([[ 100 , 1 ], [ 120 , 1 ], [ 130 , 0 ], [ 140 , 0 ]]) # Weight, Color (1 for red, 0 for yellow) y_initial = np.array([ 1 , 1 , 0 , 0 ]) # 1 for apple, 0 for banana # Initial model training clf.fit(X_initial, y_initial) # Simulate new data arriving for continual learning X_new_data = np.array([[ 110 , 1 ], [ 150 , 0 ]]) # New data points # Update the model with new data y_new_data = np.array([ 1 , 0 ]) # The true labels for the new data # Continual learning (updating the model with new data) clf.fit(X_new_data, y_new_data) # Make predictions on new data new_predictions = clf.predict(X_new_data) print ( "Predicted labels for new data:" , new_predictions) |
Output :
Continual Learning in Machine Learning
As we know Machine Learning (ML) is a subfield of artificial intelligence that specializes in growing algorithms that learn from statistics and make predictions or choices without being explicitly programmed. It has revolutionized many industries by permitting computer systems to understand styles, make tips, and perform tasks that were soon considered the extraordinary domain of human intelligence.
Traditional devices getting to know patterns are normally trained on static datasets and their know-how is fixed as soon as the prior process is finished. However, it is dynamic and continuously converting. Continual getting to know addresses the need for system mastering models to confirm new records and duties over time and make it an important concept inside the evolving subject of AI.
Table of Content
- What is Continual Learning?
- Types of Continual Learning
- Process of Continual Learning
- Implementing Continual Learning in Machine Learning
- Advantages of Continual Learning
- Limitations and Challenges of Continual Learning:
- Future of Continual Learning