/AI-Hand-Recognition-Study-Notebook

Notebook references in learning how to build image recognition models as a beginner from the fundamentals to application

Primary LanguageJupyter Notebook

AI Learning Notebooks from Basics to Practical Use (Hand Recognition)

Why Made This

Repository that will record my progress in learning artificial intelligence, machine learning, deep learning, neural networks, and utilization of Python learning libraries and frameworks towards building a hand sign recognition.

In the future, this repository will serve as an accessible learning material for Machine Learning and applying it in a real-world use case. It aims to guide people from the (1) fundamentals of the machine learning and deep learning concepts; (2) understanding, building and training neural networks to output efficient mathematical models; (3) utilization of relevant Python learning libraries and frameworks; and (4) applying learned theories and practical knowledge towards building a hand sign recognition model.

What are Its Contents

Our Jupyter Notebook repository will contain the following learning materials in Jupyter Notebook platform upon completion:

  • Machine Learning Fundamentals - (IN PROGRESS)
  • Deep Learning Fundamentals - (IN PROGRESS)
  • Neural Networks Fundamentals - (PENDING)
  • Convolutional Neural Networks Fundamentals - (PENDING)
  • Deep Learning Model Optimization and Tuning - (PENDING)
  • Fundamentals in Building Computer Vision Applications using OpenCV - (PENDING)
  • Image Recognition Fundamentals using Keras and TensorFlow - (PENDING)
  • Application of TensorFlow in Deep Learning - (PENDING)
  • Application of Keras in Deep Learning - (PENDING)

How to Access the Learning Materials

To access the learning materials, it is important to learn and understand how to use Jupyter notebooks since all of the learning materials are written in this file format. The rationale behind this is to (1) facilitate easy distribution to other people, (2) provide convenient demonstration of each code written in the notebook, and (3) allow live previews in the internal Github website or application.

For learning references in the installation steps of Jupyter Lab / Notebook in your local computer, refer to the links provided below:

Final Notes

Repository is a WORK IN PROGRESS. Additional help will be much appreciated. Let's help each other in building a free educational center-point for machine learning.