How To Get Deep Learning Results Without Spinning New Wheels.

In machine learning, to train a neural model, one typically needs a lot of data. This is challenging for many clients, as access to that data isn't always easy- this is when transfer learning comes in handy. Transfer Learning: Reusing a previously trained model on a new problem, especially when the new problem has a similar structure or features to that of the target domain. This is particularly valuable in the field of data science, as most real-world situations do not require millions of labeled data points to train complicated models. Blinx AI's transfer learning feature is one of the best ways to speed up and reduce the cost of training your AI model.