TensorFlow Learning NoteBook

This repository contains Jupyter Notebooks for learning TensorFlow, a library for training and deploying machine learning models.

Overview

TensorFlow provides a flexible and accessible platform for machine learning development in JavaScript or Python. This project aims to provide a structured learning path using Jupyter Notebooks, covering various aspects of TensorFlow, including:

• Linear Algebra
• Basics of TensorFlow
• Building and training neural networks
• Transfer learning and fine-tuning pre-trained models
• Deploying models in web applications
• Integration with other JavaScript libraries and frameworks

Notebooks

1. 01-linear-algebra.ipynb: Introduction to TensorFlow.js, setting up the environment, and basic operations.
2. 02-building-neural-networks.ipynb: Creating and training neural networks using TensorFlow.js.
3. 03-transfer-learning.ipynb: Implementing transfer learning with TensorFlow.js for specialized tasks.
4. 04-deployment-in-web-apps.ipynb: Deploying TensorFlow.js models in web applications.
5. 05-integration-with-other-libraries.ipynb: Integrating TensorFlow.js with other JavaScript libraries and frameworks.

Each notebook includes detailed explanations, code examples, and exercises to reinforce learning.

Usage

To use these notebooks, follow these steps:

1. Clone this repository to your local machine:

git clone https://github.com/N1k0l1n/Ml-Notebook.git

Contribution

Contributions to improve existing notebooks or add new ones are welcome! If you find any issues or have suggestions for improvement, please open an issue or submit a pull request.

License

This project is licensed under the MIT License.