Fit the model to training data and monitor performance. Use validation data to tune hyperparameters.
history = model.fit(X_train, y_train, validation_split=0.2)
🚀 Quick Start Demo
💻 Live Code Editor
import tensorflow as tf
from tensorflow import keras
import numpy as np
# Generate sample data
X = np.random.random((1000, 10))
y = np.random.random((1000, 1))
# Build model
model = keras.Sequential([
keras.layers.Dense(64, activation='relu'),
keras.layers.Dense(32, activation='relu'),
keras.layers.Dense(1, activation='linear')
])
# Compile
model.compile(optimizer='adam',
loss='mse',
metrics=['mae'])
# Train
history = model.fit(X, y,
epochs=50,
validation_split=0.2,
verbose=0)
print(f"Final loss: {history.history['loss'][-1]:.4f}")
Output:
📊 Training Progress
Epoch: 0/50Loss: --
✓
Install TensorFlow
Get TensorFlow 2.x installed and verify GPU support if available
Load Sample Data
Import a simple dataset like MNIST or create synthetic data
Build First Model
Create a simple 2-3 layer neural network with ReLU activations
Train & Validate
Fit the model and monitor training/validation loss curves
Make Predictions
Use trained model to make predictions on new data
Visualize Results
Plot training curves and analyze model performance