AI Code

Hands-on implementations for neural networks, natural language processing, and machine learning algorithms using TensorFlow, PyTorch, and Scikit-learn.

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Neural Network Implementation

This example demonstrates a simple neural network using TensorFlow to classify MNIST digits.

tensorflow.js 

import tensorflow as tf
from tensorflow.keras.datasets import mnist

# Load and normalize data
(images, labels), (test_images, test_labels) = mnist.load_data()
images = images / 255.0

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

# Compile model
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Train
model.fit(images, labels, epochs=5, batch_size=32)

# Evaluate
test_loss, test_acc = model.evaluate(test_images, test_labels)
print(f"Accuracy: {test_acc:.4f}")
View Advanced Examples

Network Architecture

This simple network contains 784 input units (28x28 pixels), 128 hidden units, and 10 output units (digit classes). The ReLU activation introduces nonlinearity between the layers.

  • Input Layer: 784 Neurons
  • Hidden Layer: 128 Neurons (ReLU)
  • Output Layer: 10 Neurons (Softmax)
Input
128 Neurons Layer
Output

Advanced Implementations

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Ready to Code?

Clone the repository, install dependencies, and modify these examples to suit your specific use case.

GitHub Repo
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