ML Examples

Explore practical examples and learn how to build machine learning models with Leaf.

Market Analysis AI

Advanced

Advanced financial analysis tool that predicts market trends and stock performance using multiple data sources.

import leaf as lf

# Create market analysis model
model = lf.MarketAnalyzer(
    data_sources=['price_history', 'company_financials', 'news_sentiment'],
    prediction_horizon=30  # days
)

# Add specialized layers for financial analysis
model.add_layers([
    lf.layers.TimeSeriesEncoder(128),
    lf.layers.AttentionLayer(),
    lf.layers.MarketTrendPredictor()
])

# Train on historical market data
model.train(
    market_data=historical_data,
    validation_split=0.2,
    epochs=100
)

# Make predictions
predictions = model.predict_trends(current_market_data)

Key Features:

Real-time market data processing
Multi-factor analysis
Sentiment analysis integration
Risk assessment metrics

Financial Analysis Learn more →

More Examples

Image Classification

Beginner

Train a model to classify images using a convolutional neural network.

import leaf as lf

model = lf.Sequential([
    lf.layers.Conv2D(32, kernel_size=3, activation='relu'),
    lf.layers.MaxPooling2D(),
                                    lf.layers.Flatten(),
    lf.layers.Dense(10, activation='softmax')
])

model.train(images, labels, epochs=10)

Computer Vision Learn more →

Text Generation

Intermediate

Create a language model that can generate text sequences.

import leaf as lf

model = lf.Sequential([
    lf.layers.Embedding(vocab_size, 256),
    lf.layers.LSTM(512, return_sequences=True),
    lf.layers.Dense(vocab_size, activation='softmax')
])

model.train(text_data, epochs=20)

NLP Learn more →

Time Series Prediction

Intermediate

Predict future values in a time series using LSTM networks.

import leaf as lf

model = lf.Sequential([
    lf.layers.LSTM(64, input_shape=(sequence_length, features)),
    lf.layers.Dense(1)
])

model.train(time_series_data, epochs=50)

Time Series Learn more →

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