TensorFlow Transfer Learning: Classify Images with Mobilenet and Python

In this tutorial, you'll learn how to use TensorFlow and Mobilenet to classify images through transfer learning. Transfer learning is a technique that allows you to take pre-trained models and adapt them to new use cases, making it an efficient way to build powerful image recognition systems. We'll start by installing the necessary Python libraries, and then walk you through the process of training a Mobilenet model to classify images. You'll learn how to preprocess the image data, fine-tune the model, and evaluate its performance using validation data. By the end of this tutorial, you'll have a solid understanding of how to use TensorFlow and Mobilenet for image classification, and be able to apply these techniques to your own image recognition projects. Whether you're a beginner or an experienced data scientist, this tutorial will give you the tools you need to build sophisticated image recognition systems with ease.

Code for this video: https://github.com/feitgemel/TensorFlowProjects/tree/master/transfer-learning/MobileNet

More relevant content in this playlist : https://www.youtube.com/playlist?list=PLdkryDe59y4Ze9_12JhWu3cs-lOGYwYeD

Chapters :
00:00 Intro
00:18 Installation - Python libraries 
02:15 Discover the Mobile-Net classes 
03:37 Configure the Mobile-Net model
09:03 Predict classes in images
13:13 Show the images 

 ~~~~~~~~~~~~~~~ recommended courses and books ~~~~~~~~~~~~~~~
A perfect course for learning modern Computer Vision with deep dive in TensorFlow , Keras and Pytorch . You can find it here : http://bit.ly/3HeDy1V 
I also recommend this book for deep dive in deep learning : https://amzn.to/3TTZym7

I am using one of these desktop cameras in my projects :
Logitech C270 , Simple and basic camera : https://amzn.to/37CAPjO
Logitech HD Pro Webcam C920 , A very good camera : https://amzn.to/3rM1Lof

I recommend using the Jetson Nano developer kit. You can find it here  : https://amzn.to/3k89Jni
I am using a Samsung memory card :https://amzn.to/37JHV66

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