LETS DIVE IN!

In this course, you’ll build a program that can recognize handwritten digits by training it with real-world data. By the end of this course, you’ll have your own working AI model and a better understanding of how AI works.

Terminal Installation 

Verify python:

python3 --version or python --version

Install python if not present:

winget install Python.Python.3

Install pip:

python -m ensurepip --upgrade

Instal needed modules:

pip install tensorflow

pip install keras 

First, the image needs to be turned into data that can be easily read by a machine. 

It’s turned into pixels from a range of colours into only 2. This makes processing the data easier.

It also resizes the image to a uniform size.

AI uses a series of ‘layers’ to extract features from an image.

Convolutional Layers

Identifies patterns like edges, corners, textures, or shapes.

Pooling Layers

‘Summarizes’ regions. Removing unnecessary details while keeping important information.

Flattening

Converts the 2D image into 1D to analyze it better.

The AI model passes extracted features into dense layers to learn relationships between features and labels them.

These features may include objects shapes or textures.

In this case, shapes.

The rectified linear unit recognizes complex patterns.

Softmax sorts the output into possible classes. (1,2,3)

The class with the highest probability is then chosen as the predicted label (number).

The AI is trained on a set of images where each image has already been given a label

The loss functions measures how wrong the predictions are

The AI learns by repeating this process and adjusting its predictions.
Each repeat is called an Epoch.

The more Epochs the more accurate the test is.

After training, the AI evaluates new images by extracting them and comparing them with already labeled patterns.

The model assigns a score to each label

Measures how often the model is correct.

How fat off some of the model's predictions are (loss).

First import the needed libraries.

from tensorflow.keras.datasets import mnist

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense, Flatten

Loads the MNIST dataset, which contains images of handwritten digits (0–9).

Splits the data into two parts:

Training data: Used to teach the model.

Test data: Used to evaluate how well the model has learned.

(X_train, y_train), (X_test, y_test) = mnist.load_data()

Convert the pixel values from 0-255 to 0-1. 

This converts colourful pixels to only black and white

Making it easier for the AI to process it.

X_train, X_test = X_train / 255.0, X_test / 255.0

Turn the image into a 28x28 image

Add dense layers to learn relationships between shapes and colours and sorts them into digits 0-9

Softmax turns the output into a probability

model = Sequential([

    Flatten(input_shape=(28, 28)),

    Dense(128, activation='relu'),

    Dense(10, activation='softmax')

])

Uses the ‘Adam’ model to improve accuracy

Tracks the loss and accuracy of the prediction

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

Repeat the model X times according to the epoch

model.fit(X_train, y_train, epochs=5)

Runs and outputs the accuracy

loss, accuracy = model.evaluate(X_test, y_test)

print("Test Accuracy:", accuracy)

from tensorflow.keras.datasets import mnist

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense, Flatten

# Load and preprocess data

(X_train, y_train), (X_test, y_test) = mnist.load_data()

X_train, X_test = X_train / 255.0, X_test / 255.0

# Build the model

model = Sequential([

    Flatten(input_shape=(28, 28)),

    Dense(128, activation='relu'),

    Dense(10, activation='softmax')

])

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# Train the model

model.fit(X_train, y_train, epochs=30)

# Evaluate on test data

loss, accuracy = model.evaluate(X_test, y_test)

print("Test Accuracy:", accuracy)