Transfer Learning | Deep Learning Tutorial 27 (Tensorflow, Keras & Python)

TL;DR

Transfer learning involves using pre-trained models to retrain them for new problems, saving computation power and achieving high accuracy in fewer iterations.

Transcript

Transfer learning has become quite popular in the field of image classification and Natural Language Processing. Here we take a pre-trained model and then we try to retrain it for the new problem. So if you remember from our data augmentation tutorial, we had flowers dataset where we are trying to classify five type of flowers. So in this video we ... Read More

Key Insights

• 🔋 Transfer learning is a powerful technique that saves computation power and time in image classification and NLP tasks.
• 🚅 Pre-trained models, such as Mobilenet V2, trained on large datasets can be retrained for new problems by freezing most layers and modifying the last layers.
• 🚄 Transfer learning allows for high accuracy in fewer epochs compared to training models from scratch.
• 👉 It is important to choose the right pre-trained model and modify the correct layers for the specific problem at hand.

Questions & Answers

Q: What is transfer learning?

Transfer learning is the practice of using knowledge gained from solving one problem and applying it to a related but different problem. It involves retraining pre-trained models for new tasks.

Q: How does transfer learning save computation power?

Pre-trained models are trained on large datasets with millions of images, which would take a long time to train from scratch on a regular computer. By utilizing pre-trained models, one can save time and resources as only the last layers need to be trained for the new problem.

Q: What is the purpose of freezing layers in transfer learning?

Freezing layers means that the weights in those layers do not change during training. This allows the model to use the pre-learned features extracted by the frozen layers while only adapting the last layers for the specific task at hand.

Q: How can transfer learning be applied to image classification?

In image classification, transfer learning involves taking a pre-trained model, freezing most of its layers, and retraining only the last layers for the new problem. This allows the model to benefit from the pre-learned features and achieve accurate classifications with fewer iterations.

Key Insights:

• Transfer learning is a powerful technique that saves computation power and time in image classification and NLP tasks.
• Pre-trained models, such as Mobilenet V2, trained on large datasets can be retrained for new problems by freezing most layers and modifying the last layers.
• Transfer learning allows for high accuracy in fewer epochs compared to training models from scratch.
• It is important to choose the right pre-trained model and modify the correct layers for the specific problem at hand.
• Transfer learning can be applied to various computer vision and NLP problems to achieve accurate results.

Summary & Key Takeaways

• Transfer learning is a popular technique in image classification and NLP, where pre-trained models are used to solve new problems.

• By retraining pre-trained models, high accuracy can be achieved in fewer epochs, saving computation power and time.

• Transfer learning involves freezing most layers of the pre-trained model and making changes only to the last layers for the new problem.