Transfer Learning and It’s Benefits What is Transfer Learning Transfer learning is a machine learning technique in which a model created for one job is used to another task as the foundation of the other task. In other word we could train a trained on other dataset model to our very own dataset that may or may not come from the same distribution without having us to first train the model on the trained data if needed. Given the enormous computational time and resources needed to develop neural network models especially on a computer vision and natural language processing model using pre-trained models as the starting point is a popular deep learning approach nowadays. Benefit of Transfer Learning 1. Faster Training Times: Transfer learning can significantly accelerate the training process. This is because the model starts with a foundation of knowledge, reducing the amount of time required to learn new patterns. 2. Reduced Data Requirements: The quantity of labeled data needed to train a new model is drastically decreased using transfer learning. This is especially important for activities like image classification and natural language processing (NLP) that requires big datasets. This is because the model is include with a set of pre-trained weights from previous learning. 3. Improved Model Performance: Transfer learning could also increase the performance of the model. This is because the pre-trained model has already learned general patterns and features that can be applied to the new task. Application of Transfer Learning Transfer learning is widely used nowadays due to its huge benefit, few field that can be really benefitted by transfer learning since they are usually trained on a very large data are Image Classification, Natural Language Processing (NLP), Medical Imaging and Anomaly Detection Conclusion Transfer learning has emerged as an effective technique in the field of machine learning, it offer numerous benefits such as reduced data requirements, improved model performance and faster training times. Its applications span a wide range of domains, including image classification, NLP, medical imaging and anomaly detection. As the field of machine learning continues to improve, transfer learning is very likely to play an increasingly important role in developing a more effective and efficient Machine Learning models. Reference https://machinelearningmastery.com/transfer-learning-for-deep-learning/ https://medium.com/analytics-vidhya/transfer-learning-is-it-important-to-know-934eeb3c0ea https://www.nomidl.com/machine-learning/what-are-the-benefits-of-transfer-learning-inmachine-learning/#google_vignette
