A first approach to building a deep CNN image classifier with tensorflow library using jupyter notebook (python).
This project was part of a task I had to complete in order to be recruited for a team developing UAV autopilot systems. The task involved building a deep learning model using the TensorFlow library and Jupyter Notebook (Python) to distinguish between photos of cats and dogs in the Kaggle dataset. A detailed explanation of the project is provided in the 'documentation_CNN_image_classifier' file, and a brief PowerPoint presentation can be found in the 'presentation_CNN_image_classifier' file. The source code is located in the 'ai_demo' file, while the 'models' folder contains the serialized model, and the 'log' folder tracks the model training. The 'dummy' folder consists of screenshots of the project, and the 'ai2' folder contains the training data. Additionally, 'documentation2_ASAT' and 'presentation2_ASAT' are respectively the Greek documentation and presentation mentioned earlier. Finally, 'dogtest' and 'cattest' images are used for the model evaluation.
