Using transfer learning
Throughout the years, countless organizations, research entities, and contributors within the open-source community have meticulously built sophisticated models for general use cases. These models, often trained with vast amounts of data, have been optimized over years of hard work and are suited for various applications, such as:
- Detecting objects in videos or images
- Transcribing audio
- Analyzing sentiment in text
When initiating the training of a new ML model, it’s worth questioning, rather than starting from a blank slate, whether we can modify an already established, pre-trained model to suit our needs. Put simply, could we leverage the learning of existing models to tailor a custom model that addresses our specific needs? Such an approach, known as transfer learning, can provide several advantages:
- It gives a head start to our model training.
- It potentially enhances the quality of our model by utilizing...
