Handwritten Digit Recognition using CNN (Convolutional Neural Network
Last updated:
0 purchases
$89.00
Description:
Handwritten digit recognition involves using machine learning models, specifically CNNs, to classify images of handwritten digits (0-9). This task is often solved using datasets like MNIST.
Features:
- Recognize handwritten digits (0-9).
- Train and test on the MNIST dataset.
- Use CNN to extract features and improve accuracy.
Requirements:
- Programming Language: Python
- Libraries/Tools:
- TensorFlow or Keras for building and training the CNN model.
- NumPy, Matplotlib for data manipulation and visualization.
- MNIST dataset (can be accessed via Keras).
Instructions:
- Load the MNIST dataset.
- Preprocess the data (normalize images, flatten images if necessary).
- Build a CNN model using layers like Conv2D, MaxPooling2D, Flatten, and Dense.
- Train the model on the training data and evaluate it on test data.
License:
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
Files In This Product: (if this is empty don't purchase this product)
Customer Reviews
There are no reviews.
