Deep learning has gained prominence due to its ability to achieve state-of-the-art performance in various complex tasks, yet it requires vast amounts of data to train models with numerous parameters effectively. A key challenge is overcoming the data requirements for deep learning, which can be addressed through transfer learning, a technique that leverages models pre-trained on large datasets for use in different, but related, tasks. This approach is particularly beneficial for domains with limited data, allowing smaller networks to be trained effectively by utilizing features learned from a broader dataset. Transfer learning not only reduces the data needed but also enhances model generalization, although it demands considerable expertise to implement effectively. Despite its advantages, transfer learning remains underutilized outside specialist circles, highlighting the need for greater awareness and accessibility to its potential, as exemplified by platforms like NanoNets which simplify the process by offering pre-trained models that can be customized with user data.