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The world is fast-paced and requires matching speed in assisting computerized processes. Machine Learning comprises multiple models aimed at solving specific problems. An effective approach to improving the performance of Machine Learning models is by using the previously trained models on newer tasks of a similar nature. The approach is currently in practice and referred to as transfer learning.
Here’s what we’ll cover:
Transfer learning is an effective and efficient approach to pace up the Machine Learning process. The generalized training of Machine learning involves training the models from scratch for each task. It requires specific and processed data, considerable time, and computational resources. Once generated, the models are referred to as pre-trained models and are capable of various tasks such as image, pattern, and speech recognition. These pre-trained models can be further modified to perform computer vision tasks, healthcare, autonomous vehicles, and others.
The practical applications of integrating automated Machine Learning techniques with the transfer learning approach result in the development of autoML transfer learning. The higher efficiency models thus developed find the following applications in the real world:
The applicability is seen in different domains. Here is how:
Healthcare: Transfer learning is of immense importance in medical image analysis and diagnosis owing to the usage of pre-trained models. They can be applied to specific medical conditions like MRIs, X-rays, CT scans, and others.
Autonomous vehicles: Usage of pre-trained models for recognizing the traffic signs from cameras, pedestrians, and objects is possible. It helps to create robust perception systems.
Finance: The previous training is important for fraud detection, stock price prediction, and credit scoring. Further, the pre-trained models can also be fine-tuned for specific financial tasks.
Manufacturing: Improving the quality through quality control, predictive maintenance, and detecting defects can be easily performed by fine-tuning the models.
Telecommunications: The improvising techniques can be used for network intrusion detection, network optimization, and customer churn prediction. The patterns are indicative of network anomalies or predicting customer behavior.
Cybersecurity: It is associated with protecting from malicious activities for network security, malware, and anomaly detection.
Education: Transfer learning also finds applications in educational technology for different tasks like automated grading, student performance prediction, and personalized learning. They can be further adapted for student data analysis and providing tailored educational experiences.
Social media monitoring: Here, it is used to detect sentiments, brand mentions, and trends.
Retail: Adapting the models for image recognition tasks such as shelf-stocking and product recognition, the transfer learning benefits retailers too. It is helpful in inventory management, demand forecasting, and customer sentiment analysis.
There are two methods to use the transfer learning approach: the developed model and the pre-trained model approach. The basic difference is the requirement to develop the model first in developing the model approach with the intentional, strategic pathway for transfer learning. The pre-trained model approach uses an already available pre-trained model, making it inferior compared to the developed model approach.
Developed model approach: It requires selecting the related predictive modeling problem and developing a novel but improved model based on it. The new model is trained according to the first task, and it can be further used as a starting point for the model on the second task. It adapts the pre-trained model according to the second task. Further, fine-tune the model as per the requirements.
Pre-trained model approach: It involves choosing the pre-trained source model from an already available source. The reuse of the model is further accompanied by tuning the model for the task of interest.
The pre-trained model approach can be used in any of three ways: prediction, feature extraction, and fine-tuning. In prediction, the model is downloaded and used for image classification. It can be done using the ResNet50 for ImageNet classification. The feature extraction uses the output of the layer before the final layer to input to the new model. The features are transferred to the new classifier. The method is not efficient. It requires fine-tuning the new classifier to improve the accuracy.
Transfer learning incorporates using pre-trained models. There are multiple expected benefits, enlisted as follows:
What is transfer learning, what are its applications, how to use it, and other important concepts are covered above. However, there are more in-depth concepts to the topic. Covering them is vital to shine out in the current market requiring specialization and deep knowledge. Interested candidates must look forward to understanding the associated techniques and approaches to know the basics of usage. The work must be done in accordance with preparation for the questions fired in interviews.
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Ans. A real-life example is seen in the e-commerce industry, using video game simulations for training the robots, using pre-trained models for dog identification to identify the cats, sentiment analysis, spam filtering, and others.
Ans. The disadvantages of transfer learning are overfitting, domain mismatch, and limited transferability.
Ans. Transfer learning refers to leveraging the knowledge of the source task and applying it to the target task. Fine-tuning is the step within transfer learning that includes training and adjusting the pre-trained model on the target task.
Ans. Transfer learning is more commonly associated with supervised learning. Although the application is possible in both types of learning.
Ans. Ensemble learning, data augmentation, domain adaptation, and training from scratch are the alternatives to transfer learning.
Ans. CNN is a type of neural network architecture that learns from hierarchical patterns and features in input data. Transfer learning is the training of a model on one task and using it on another task.
Ans. Transfer learning can not be used if there is limited data, little labeled data, computational constraints, and complexity of the source task.
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