Lecture

Summary of Course Highlights

What is an AI Model?

A computer program that analyzes given data to learn patterns and rules, subsequently making predictions and decisions based on the new data.


What is Fine-Tuning?

The process of retraining an already trained AI model to improve its performance for specific tasks or purposes.


What Does it Mean for AI to Learn?

It means extracting features from numerous examples to learn patterns and developing the ability to process new data accurately.

Technical Explanation: It involves creating an algorithm (a step-by-step procedure for performing tasks) that determines the output for newly inputted data.


The Role of Weights and Biases

AI adjusts its parameters, including weights and biases, to learn the data patterns and make predictions for new data.

TermDescription
WeightsDetermine the importance of specific features in input data
BiasesValues that adjust the model to prevent outputs from becoming biased in a specific direction
Equation for Weights and Biases
y = w1x1 + w2x2 + ... + wnxn + b

What is JSON?

A lightweight data format used for storing and exchanging data.

JSON consists of objects and arrays, with objects enclosed in curly braces { } and arrays in square brackets [ ].

JSON Example
// An array enclosed in square brackets [ // Objects enclosed in curly braces { "name": "John Doe", "math": 85, "english": 90 }, { "name": "Jane Smith", "math": 88, "english": 80 } ]

A JSONL file used for fine-tuning stores one JSON formatted data entry per line.


What is a Dataset?

A collection of data gathered and organized for specific purposes, such as AI model training and validation.

ComponentDescription
FeaturesInput data that the model learns from
LabelsCorrect answers for each input data
MetadataDocumentation providing additional information like data source, creation date, etc.

Types of Datasets

ComponentDescription
Training DatasetUsed to train the model
Validation DatasetUsed to evaluate model performance during training
Test DatasetUsed to test model performance
  • Training Dataset: About 60-80% of the total data

  • Validation Dataset: About 10-20% of the total data

  • Test Dataset: About 10-20% of the total data


What is a Loss Function?

A function that measures the difference between model predictions and actual values, taking predictions and actual values (correct answers) as input and returning the loss (error) as output.

A smaller value of the loss function indicates that the model's predictions are closer to the actual values, and the goal of AI training is to minimize the value of the loss function.

The gradient of the loss function, known as the gradient, informs how to adjust the model's parameters to reduce the loss.


Hyperparameters

Parameters (settings) established when training an AI model.

Key HyperparametersDescription
Learning RateControls the speed of model training
Batch SizeNumber of data points processed at once
Number of EpochsThe number of times the entire dataset is iterated over during training

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