How transfer learning accelerates model training in ML projects

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How transfer learning accelerates model training in ML projects

rhutvik14
The boon of transfer learning has emerged in machine learning, enabling developers to access models that have the benefit of gained knowledge, and quickly transfer it to new tasks. Rather than developing models from the start, developers can leverage existing neural networks, trained on petabytes of data, and tune them to a domain-specific application. Using transfer technology, enterprise developers spend less time collecting limited amounts of labeled data and have a drastically shortened model training time, yet achieve comparable accuracy. Developers who are interested in transfer learning, especially in the enterprise field, may want to take an Artificial Intelligence Course in Pune to learn more about transfer techniques and their heart-felt applications in enterprises and start-ups.





The critical benefit of transfer learning is not only a pre-trained model, but generalizing knowledge. Companies using pre-trained models such as BERT, ResNet, and GPT have achieved benchmarks simply by adjusting the pre-trained model to perform a task. A powerful benefit of pre-trained models is that organizations can reuse learned features in the form of reports, which decreases computing costs, reduces cycle times for their deployment, improves affordably scaling and helps develop machine learning solutions faster. Even short hands-on labs and hands-on guided projects under Artificial Intelligence Training in Pune give learners the smarts to apply transfer learning methods in their ML projects outside of their course., In domains where data is scarce transfer learning will help get innovators on their feet more quickly and enable them to build value themselves sooner.


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