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Is your feature request related to a problem? Please describe.
Currently, the Neural Networks folder lacks a comprehensive example of image classification using Convolutional Neural Networks (CNNs) on standard datasets. Beginners often find it challenging to understand CNNs without practical, hands-on examples, especially on widely-used datasets like CIFAR-10.
Describe the solution you'd like
I would like to add a project under the Neural Networks folder that demonstrates image classification using CNNs, specifically utilizing the CIFAR-10 dataset. This will serve as an accessible introduction to CNN architecture for users, showcasing essential concepts such as convolutional layers, pooling, activation functions, and model evaluation.
Describe alternatives you've considered
An alternative would be to implement image classification on a simpler dataset (e.g., MNIST) or a more complex one (e.g., ImageNet). However, CIFAR-10 provides a balanced intermediate level, allowing users to understand CNNs without an overwhelming amount of data or complexity.
Approach to be followed (optional)
Load and preprocess the CIFAR-10 dataset.
Build a CNN model using layers such as Conv2D, MaxPooling2D, Flatten, and Dense.
Compile the model, specifying the optimizer, loss function, and evaluation metrics.
Train the model on the CIFAR-10 training data and validate it on test data.
Evaluate the model’s performance, and provide visualizations of sample predictions.
Document the code to guide users through the steps.
Additional context
This feature would enhance the Neural Networks folder by providing a hands-on, structured introduction to CNNs, helping users understand how to apply deep learning to image classification tasks. A sample architecture or learning curves could be included to visually aid understanding.
The text was updated successfully, but these errors were encountered:
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Is your feature request related to a problem? Please describe.
Currently, the Neural Networks folder lacks a comprehensive example of image classification using Convolutional Neural Networks (CNNs) on standard datasets. Beginners often find it challenging to understand CNNs without practical, hands-on examples, especially on widely-used datasets like CIFAR-10.
Describe the solution you'd like
I would like to add a project under the Neural Networks folder that demonstrates image classification using CNNs, specifically utilizing the CIFAR-10 dataset. This will serve as an accessible introduction to CNN architecture for users, showcasing essential concepts such as convolutional layers, pooling, activation functions, and model evaluation.
Describe alternatives you've considered
An alternative would be to implement image classification on a simpler dataset (e.g., MNIST) or a more complex one (e.g., ImageNet). However, CIFAR-10 provides a balanced intermediate level, allowing users to understand CNNs without an overwhelming amount of data or complexity.
Approach to be followed (optional)
Additional context
This feature would enhance the Neural Networks folder by providing a hands-on, structured introduction to CNNs, helping users understand how to apply deep learning to image classification tasks. A sample architecture or learning curves could be included to visually aid understanding.
The text was updated successfully, but these errors were encountered: