-
Create MNIST_Digit_Recognizer.ipynb
Code Walkthrough
1. Dataset Loading:
python
Copy code
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train and X_test: Images of handwritten digits.
y_train and y_test: Corresponding digit labels.
2. Data Preprocessing:
python
Copy code
X_train = X_train.reshape(60000, 28, 28, 1)
X_test = X_test.reshape(10000, 28, 28, 1)
y_train_one_hot = to_categorical(y_train)
y_test_one_hot = to_categorical(y_test)
Reshaping: Adds a "channel" dimension to images (required by CNNs).
Normalization: Automatically done by to_categorical.
One-Hot Encoding: Converts labels into vectors.
3. Building the CNN Model:
python
Copy code
model = Sequential()
model.add(Conv2D(64, kernel_size=3, activation='relu', input_shape=(28,28,1)))
model.add(Conv2D(32, kernel_size=3, activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(10, activation='softmax'))
Conv2D: Applies filters (kernels) to extract image features.
First layer: 64 filters, each 3x3.
Second layer: 3… -
initial commit
