diff --git a/Flux.jl b/Flux.jl
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+++ b/Flux.jl
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+using Flux
+using Statistics
+
+# Generate some example data (y = 2x + 1)
+x = rand(100)  # Random input data
+y = 2 * x .+ 1 .+ 0.1 * randn(100)  # Output data with some noise
+
+# Define the model: A simple linear layer with one input and one output
+model = Chain(
+    Dense(1, 1)  # Dense layer with 1 input and 1 output
+)
+
+# Loss function: Mean Squared Error (MSE)
+loss(x, y) = Flux.mse(model(x), y)
+
+# Training loop
+optimizer = ADAM()  # Optimizer choice (Adam)
+
+# Prepare the data (reshape to column vectors)
+x_data = reshape(x, 100, 1)
+y_data = reshape(y, 100, 1)
+
+# Train the model
+for epoch in 1:1000
+    Flux.train!(loss, params(model), [(x_data, y_data)], optimizer)
+    if epoch % 100 == 0
+        println("Epoch $epoch, Loss: $(loss(x_data, y_data))")
+    end
+end
+
+# After training, you can use the model to predict
+new_x = [0.5, 1.5, 2.5]  # New input data
+predictions = model(reshape(new_x, length(new_x), 1))
+
+println("Predictions for new data: $predictions")