Screenshot captures Visual Studio Code (VS Code) editor environment in dark theme. Central pane shows Python script containing imports, function definitions, and loop structures. Syntax highlighting is applied: keywords in purple, variables in white, strings in orange, and functions in blue-green.

Script begins with imports: import numpy as np, import tensorflow as tf, along with supporting libraries. Code defines function create_dataset which loads and normalizes data, shuffles, batches, and returns prepared dataset. Function employs TensorFlow dataset API (tf.data.Dataset.from_tensor_slices) and pipeline transformations such as shuffle, batch, and prefetch.

Subsequent section defines neural network model using Keras Sequential API. Layers include Dense layers with ReLU activations and final output layer with softmax activation. Optimizer is Adam, loss function is categorical crossentropy, and metrics include accuracy. Model is compiled and prepared for training.

Training loop uses .fit() method, specifying dataset, number of epochs, and validation data. Log outputs such as loss and accuracy are set to display per epoch.

Lower portion of script contains evaluation and prediction routines, including call to model.evaluate on test dataset and model.predict on new data samples. Code includes conditional if __name__ == "__main__": block, standard in Python scripts for main execution.

VS Code interface displays file path in tab labeled deep_learning_model.py. Explorer panel on left reveals workspace directory structure with src, data, and config folders. Top bar shows open command palette with options for Python interpreter selection.

Overall, screenshot demonstrates workflow of deep learning implementation in Python using TensorFlow, organized within modular script inside modern IDE environment.