Welcome to the Neural Network Example Code tutorial! Below is a simple guide to help you get started with building and training a basic neural network using Python and TensorFlow/Keras.

Step-by-Step Guide

  1. Install Dependencies 🛠️
    Start by installing the required libraries: 

    pip install tensorflow numpy

    🔗 Learn more about TensorFlow installation

  2. Prepare Data 📁
    For demonstration, we'll use the MNIST dataset: 

    from tensorflow.keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

    MNIST Dataset

  3. Build the Model 🏗️
    Define a simple neural network architecture: 

    model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

    Neural Network Structure

  4. Compile and Train 🚀
    Configure the model and start training: 

    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)

    Training Progress

  5. Evaluate Model 📊
    Test the model's performance on unseen data: 

    test_loss = model.evaluate(x_test, y_test)
print(f"Test Loss: {test_loss}")

    Model Evaluation

Expand Your Knowledge

Want to dive deeper into deep learning concepts? Check out our Deep Learning Basics tutorial for foundational knowledge!

Happy coding! 🌟