How Does Fine-Tuning Work?
Fine-tuning is the process of taking a pre-trained AI model and further training it for a specific task or specialized field.
In this lesson, we will explore the overall process of fine-tuning.
Fine-Tuning Process
1. Model Initialization
The existing model's weights and biases are used as the initial values.
For instance, in a spam message classification AI, the fine-tuning starts with the model already understanding the patterns it has previously learned.
2. Fine-tuning Configuration
Settings for training the model with new data are determined. One of the key settings is the learning rate, which defines the speed at which the model learns from the data.
If the learning rate is too high, the model may not learn the data properly; if too low, the training process may take too long.
These settings are known as hyperparameters, and beyond learning rate, they include batch size, number of epochs, and more, to optimize the model's learning.
Epoch
An epoch refers to the number of times the model goes through the entire dataset during training.
For instance, if the epoch is set to 5, the model will learn from the entire training dataset five times.
Too few epochs may not allow the model to learn adequately from the data, while too many epochs can result in overfitting where the model becomes specialized in the training data alone.
Batch Size
Batch size is the amount of data fed into the model at once.
For example, if the batch size is 32, the model processes 32 data items at a time during training.
If the batch size is too large, it may overuse computing resources (memory), and if too small, the training process might slow down.
3. Prepare Training Data
Prepare the data for fine-tuning.
For instance, in the case of a spam message classification AI, you might add more stock advertisement spam messages to enable the model to better classify such messages.
4. Train the Model
The model is further trained using the new training data. During this process, the model adjusts its pre-existing learned patterns including weights and biases.
5. Performance Evaluation
The trained model is evaluated using test data to check how accurately it predicts new data.
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