camara94/convolutional-neural-networks-tensorflow
In Course 2 of the deeplearning.ai TensorFlow Specialization, you will learn advanced techniques to improve the computer vision model you built in Course 1. You will explore how to work with real-world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and dropout. Finally, Course 2 will introduce you to transfer learning and how learned features can be extracted from models.
This project helps developers enhance their computer vision models for image classification. It takes real-world image datasets of varying sizes and outputs a more accurate, robust model capable of classifying images effectively. This is for developers building and improving AI systems that 'see' and categorize images.
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Use this if you are a developer looking to improve the performance and prevent overfitting in your existing image classification models.
Not ideal if you are looking for a pre-built, ready-to-use image classification solution without needing to write or understand code.
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MIT
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Last pushed
Apr 01, 2022
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