What is Transfer Learning? | With code in Keras

TL;DR

Transfer learning is the practice of utilizing pre-trained models to leverage their learnings for new tasks, allowing for efficient model training and solving data scarcity issues.

Transcript

you probably heard of the term transfer learning a bunch of times already but what does it mean why do people use it and how can you get started let's answer all of those questions in this video transfer learning is taking all or part of a model that is already developed on a certain task and using its learnings on a whole new task the base model t... Read More

Key Insights

• 📰 Transfer learning involves leveraging a pre-trained model's knowledge for a new task, saving time and resources.
• 👻 The layers deep within a deep neural network recognize increasingly complex features, allowing for the selection of relevant layers during fine-tuning.
• ❓ Transfer learning addresses the data scarcity problem, enabling effective training even with limited task-specific data.
• 😑 Pre-trained models can be obtained from various sources, such as TensorFlow Hub, Keras Applications, PyTorch, Hugging Face, and Model Zoo.
• 🚂 Adjusting the architecture and training the modified model completes the process of transfer learning.
• ⚾ Freezing the base model's layers during fine-tuning prevents them from being updated, ensuring focused learning of newly added layers.
• 🚄 Changing the runtime to GPU in platforms like Google Colab accelerates training speed.

Questions & Answers

Q: What is transfer learning?

Transfer learning is the process of using a pre-trained model's learnings on a specific task and applying them to a different task, optimizing model training.

Q: How does transfer learning work?

Transfer learning can involve replacing only the output layer of a pre-trained model or modifying multiple layers to fit the new task's requirements. The extent of modification depends on the similarity between the two tasks.

Q: Why is transfer learning beneficial?

Transfer learning is particularly useful when there is a scarcity of data for the target task. By leveraging the knowledge gained from a related task, models can achieve better results with limited data.

Q: How can one get started with transfer learning?

To begin with transfer learning, you can find pre-trained models online, within deep learning frameworks, or through websites and APIs. Then, customize the model by adding or modifying layers specific to your task and train it with your dataset.

Summary & Key Takeaways

• Transfer learning involves taking a pre-trained model and using its knowledge to tackle a different task, known as fine-tuning.

• Fine-tuning entails adjusting the pre-trained model by removing or adding layers to suit the new task's similarity to the original task.

• Transfer learning facilitates model training with limited data, saves time, and contributes to reducing the carbon footprint of training models.