README

Folder Structure

In the code subfolder, you will find all the tutorial notebooks

code/
│
├── 00 - Hold-out example with scikit-learn.ipynb               # Basic hold-out method example using scikit-learn
├── 01 - Introduction_to_keras_tuner.ipynb                      # Introductory guide to Keras Tuner
├── 02 - Easy_Network_with_Keras.ipynb                          # Simple neural network with Keras
├── 03 - hyperparameter_tuning_with_scikit_learn.ipynb          # Hyperparameter tuning using scikit-learn
├── 04 - Exercise_Supervised_Learning_2.ipynb                   # Supervised learning exercise
├── 05 - Converting_a_model_with_TF_Lite_converter.ipynb        # Converting models with TensorFlow Lite converter
├── 06 - Quantization_of_models_Complete_examples.ipynb         # Model quantization examples
├── 07 - Retrain_MobileNet_V2_classifier_for_the_Edge_TPU.ipynb # Retraining MobileNetV2 for Edge TPU

All notebooks demonstrate different machine learning techniques, covering fundamental concepts like model training, tuning, conversion, and deployment.

Setup Instructions

Run using Colab

All notebooks are designe to un primarily on colab, you will find a run on colab at the beggining of each notebook.

Local Env

The notebook can be run on your local machine. Note : this is subject to more bugs and changes.

1. Create a Conda Environment with Python 3.12

1. Open a terminal or command prompt.
2. Run the following command:

   conda create -n ml_notebooks python=3.12

3. Activate the environment:

   conda activate ml_notebooks

4. Install dependencies:

   pip install -r requirements.txt

2. Verify Installation

1. Ensure dependencies are installed:

   pip list

2. Test the notebooks in Jupyter:

   jupyter notebook

Additional Notes

• Ensure TensorFlow and scikit-learn versions match your hardware setup.
• Additional dependencies may be required for Edge TPU support.
• If you face installation issues, update pip and conda before retrying.