Tensorflow Image Classification using Convolution neural network CNN | Implementation
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Tensorflow Image Classification using Convolution neural network CNN | Implementation
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https://codewsk.com/tomato-leaf-disea...
Tomato leafs disease classification using CNN for binary class easy explain @codewithsk8:
• Tomato leaf's disease classification ...
Hello guys,
Welcome to my channel, In this tutorial, I will show you the tomato leaf disease with classification using CNN Python.
You will learn how the Deep convolutional neural network detects the disease from images. Deep Convolutional Neural Network is utilized in this study to identify infected and healthy leaves, as well as to detect illness in afflicted plants.
The CNN model is designed to suit both healthy and sick leaves; photos are used to train the model, and the output is determined by the input leaf.
I really encourage you to watch the upcoming video to understand the whole process. and if you didn't subscribe to our channel please do so and press the bell icon to get the notification of upcoming parts. thankyou
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About the course:
TensorFlow Image Classification using Convolutional Neural Network (CNN) is a powerful approach for recognizing and categorizing images. CNNs are designed to automatically learn and extract meaningful features from raw pixel data, enabling accurate classification tasks. TensorFlow, a popular deep learning framework, provides a comprehensive set of tools and libraries to build, train, and deploy CNN models. By leveraging TensorFlow's high-level APIs and prebuilt layers, developers can efficiently construct CNN architectures, configure hyperparameters, and handle data preprocessing. This combination of CNNs and TensorFlow empowers users to tackle various image classification challenges, ranging from simple object recognition to complex visual understanding tasks.
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Watch video Tensorflow Image Classification using Convolution neural network CNN | Implementation online without registration, duration hours minute second in high quality. This video was added by user CodeWithSK 16 May 2023, don't forget to share it with your friends and acquaintances, it has been viewed on our site 1,76 once and liked it 7 people.