This is important because it prevents significant updates to the gradient. Just retrain the model or part of it using a low learning rate. However, since you have to retrain the entire model, you’ll likely overfit. Fine-tuning will usually improve the performance of the model. What is the difference between transfer learning and fine-tuning?įine-tuning is an optional step in transfer learning. You will explore these use cases in a moment. In this case, you can use a pre-trained word embedding like GloVe to hasten your development process. Furthermore, training will take a long time. The challenge here is that you might not have enough data to train the embeddings. You can train vector representations yourself. classifying text requires knowledge of word representations in some vector space.You can use these models and fine-tune them to classify insects. This is because the dataset contains over 1000 classes. models trained on the ImageNet can be used in real-world image classification problems.The advantage of pre-trained models is that they are generic enough for use in other real-world applications. As you will see later, transfer learning can also be applied to natural language processing problems. In this case, you can, for example, use the weights from the pre-trained models to initialize the weights of the new model. Transfer learning is particularly very useful when you have a small training dataset. Including the pre-trained models in a new model leads to lower training time and lower generalization error. These models can be used directly in making predictions on new tasks or integrated into the process of training a new model. The weights obtained from the models can be reused in other computer vision tasks. The pre-trained models are usually trained on massive datasets that are a standard benchmark in the computer vision frontier. Transfer learning is about leveraging feature representations from a pre-trained model, so you don’t have to train a new model from scratch. Well then, let’s start learning! (no pun intended… ok, maybe a little) What is transfer learning?
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