GPT-3.5 vs GPT-4: Which AI Wins at Complex ML Tasks?

Table of Contents

1. Introduction
2. Comparing GPT 3.5 and GPT 4
3. Project Overview
4. Dataset and Model Training
5. Building the Graphical User Interface
6. Implementing the Drawing Functionality
7. Preprocessing the Drawn Image
8. Classifying the Handwritten Digits
9. Troubleshooting and Adjustments
10. Conclusion

Introduction

In this video, we will be comparing the performance of GPT 3.5 with that of GPT 4 by using Chat GPT to implement a machine learning project. The focus will be on a complex task involving a graphical user interface and handwritten digit classification.

Comparing GPT 3.5 and GPT 4

Before diving into the project, it is essential to understand the key differences between GPT 3.5 and GPT 4. While GPT 3.5 is capable of handling the task at hand, GPT 4 offers enhanced performance and capabilities. However, it is important to note that GPT 4 requires a premium subscription, GPT Plus, to access and has certain limitations on usage.

Project Overview

The goal of the project is to build a Python application with a graphical user interface that allows users to draw handwritten digits on a canvas. The drawn images will then be classified by a machine learning model trained on the MNIST dataset, which comprises labeled handwritten digit examples. The AI implemented in the project will utilize this dataset to recognize newly drawn digits.

Dataset and Model Training

The first step of the project involves training a simple machine learning model on the MNIST dataset. This model will serve as the basis for classifying the user-drawn digits. The process will include data preprocessing, model building using TensorFlow and Keras, and model evaluation to ensure accuracy.

Building the Graphical User Interface

To enable users to draw and Interact with the application, a graphical user interface (GUI) needs to be implemented. The GUI will consist of a canvas where users can draw digits using their mouse and buttons for operations like clearing the canvas and initiating the prediction process.

Implementing the Drawing Functionality

The next task is to implement the functionality that allows users to draw on the canvas. This will involve binding the appropriate event (B1 motion of the mouse) to the drawing function. The drawn lines will be converted into images for further processing.

Preprocessing the Drawn Image

Before feeding the drawn image into the machine learning model, it is essential to preprocess it to ensure compatibility. This preprocessing step may involve resizing the image and converting it to the appropriate format. Additionally, the image may need to be inverted or adjusted to improve the classification accuracy.

Classifying the Handwritten Digits

Once the preprocessing is complete, the preprocessed image will be fed into the machine learning model for prediction. The model will classify the drawn digit as one of the ten possible handwritten digits. The predicted digit will be displayed as output to the user.

Troubleshooting and Adjustments

Throughout the implementation process, it is essential to monitor the performance and troubleshoot any issues that arise. If the predictions are not accurate, various factors such as overfitting, model complexity, or drawing quality may need to be analyzed and addressed. Adjustments may include modifying parameters, changing the line thickness, or exploring alternative model architectures.

Conclusion

In conclusion, the project demonstrated the comparison between GPT 3.5 and GPT 4 in terms of implementing a machine learning project with a graphical user interface. While both models can handle the task, GPT 4 showcased enhanced capabilities and efficiency. However, it is crucial to consider factors like model training, data preprocessing, and troubleshooting to achieve optimal results.

Now that we have reviewed the table of Contents, it's time to start diving into the article. Let's explore the project in Detail and understand each step involved.