Transfer learning is a method used when a model is developed for one task is reused to work on a second task. Fine tuning is one approach to achieve transfer learning. In Transfer Learning we train the model with a dataset and after we train the same model with another dataset that has a different distribution of classes. In Fine-tuning, an approach of Transfer Learning, we have a dataset, and we make an 80-20 split and use 80% of it in training. Then we train the same model with the remaining 20%. Usually, we change the learning rate to a smaller one, so it does not have a significant impact on the already adjusted weights. To decide which method to choose, one should experiment first by using transfer learning as it is easy and fast, and if it does not suffice the purpose, then use fine tuning.