Handwritten number recognition model using MNIST dataset is a machine learning model that can recognize handwritten digits (0-9) from images using the MNIST dataset. MNIST is a popular dataset of handwritten digits, which contains 60,000 training images and 10,000 testing images of handwritten digits.
The handwritten number recognition model is usually built using a neural network, which is a type of machine learning model that is inspired by the structure and function of the human brain. In particular, the model is built using a type of neural network called a convolutional neural network (CNN), which is designed to process images.
The CNN model consists of several layers, including convolutional layers, pooling layers, and fully connected layers. The convolutional layers are responsible for extracting features from the input image, while the pooling layers reduce the size of the feature maps. The fully connected layers are responsible for making the final classification decision.
In summary, the Handwritten number recognition model using MNIST dataset is a machine learning model that can recognize handwritten digits from images using a convolutional neural network. The model is trained on the MNIST dataset and can be used to make predictions on new data. The performance of the model is evaluated using metrics like accuracy.
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