Fine-tuning Review
Let's summarize the key points about fine-tuning we've learned so far.
Fine-tuning
A process to enhance the performance of a pre-trained AI model by retraining it for specific tasks or objectives.
Comparison of Fine-tuning and Prompt Engineering
| Feature | Fine-tuning | Prompt Engineering |
|---|---|---|
| Definition | Re-training a pre-trained model for specific tasks or datasets | Optimizing model input prompts to achieve desired output results |
| Data Requirement | Requires a large amount of domain-specific data | Can be done with relatively less data |
| Time and Cost | Significant time and computational resources for model retraining | Prompt optimization can be done in comparatively less time |
| Flexibility | Ability to create a model optimized for specific tasks | Ability to apply a single model to various tasks |
| Technical Requirements | Deep understanding of deep learning and model training | Understanding of prompt design and experimentation |
| Output Consistency | Guarantees high consistency | Output varies depending on the prompt |
| Scalability | Limited scalability to specific tasks | Flexibly applicable to various tasks |
Machine Learning
The process of creating models that can analyze data, learn patterns from it, and make predictions or decisions based on new data
Supervised Learning
A learning method where input data and corresponding correct answers are provided
Unsupervised Learning
A learning method to discover data structures or patterns from data without predefined answers
Reinforcement Learning
A learning method where actions are learned to maximize rewards through trial and error
Deep Learning
A technology where computers learn like humans, a subset of machine learning
The Meaning of AI Learning
The ability to extract features from a large amount of example data, learn patterns, and accurately process new data based on it
Technically, it means creating an algorithm (a step-by-step procedure for performing a task) to determine outputs for newly inputted data
Weights
Parameters that determine the importance of specific features in the input data
Bias
Parameters that adjust model output to prevent any bias in a specific direction
Fine-tuning
Using the weights and biases of an existing model as initial values. Adjusting weights and biases while learning from new data, building upon previously learned patterns.
Dataset
A collection of data compiled and organized for specific purposes like AI model training and validation
Training Dataset
The data that AI learns from initially
Validation Dataset
Used to evaluate the AI's performance during training
Test Dataset
Used to assess how well AI performs in real-world scenarios
Preprocessing
The process of organizing and transforming data before it's analyzed or used for AI model training
JSON
A lightweight data format used for data storage and exchange
JSONL
Short for JSON Lines, a format for storing JSON data, with one JSON object per line
Loss Function
A function that calculates the difference between the model's output and the actual correct answer
Gradient
The slope of the loss function, adjusting model weights in the direction of minimizing values
Hyperparameters
Parameters set during AI model training
1. Learning Rate
Determines how much the model changes with each iteration
2. Batch Size
The amount of data the model processes at one time
3. Epochs
Decides how many times to repeat learning for the entire dataset
Overfitting
A state where the AI model is overly optimized for the training data and poorly performs on new or validation data
Underfitting
A state where the AI model hasn't sufficiently learned patterns from the training data, resulting in poor performance on both training and new data
Transfer Learning
A technique to apply a previously learned model to a new problem
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