diff --git a/examples/train-kernel-parameters/Manifest.toml b/examples/train-kernel-parameters/Manifest.toml
new file mode 100644
index 000000000..7a3bb3837
--- /dev/null
+++ b/examples/train-kernel-parameters/Manifest.toml
@@ -0,0 +1,1088 @@
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diff --git a/examples/train-kernel-parameters/Project.toml b/examples/train-kernel-parameters/Project.toml
new file mode 100644
index 000000000..0645010d7
--- /dev/null
+++ b/examples/train-kernel-parameters/Project.toml
@@ -0,0 +1,17 @@
+[deps]
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
+ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
+KernelFunctions = "ec8451be-7e33-11e9-00cf-bbf324bd1392"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+
+[compat]
+Distributions = "0.25"
+Flux = "0.12"
+ForwardDiff = "0.10"
+KernelFunctions = "0.10"
+Literate = "2"
+Plots = "1"
+julia = "1.3"
diff --git a/examples/train-kernel-parameters/script.jl b/examples/train-kernel-parameters/script.jl
new file mode 100644
index 000000000..8966ac4cf
--- /dev/null
+++ b/examples/train-kernel-parameters/script.jl
@@ -0,0 +1,84 @@
+# # Kernel Ridge Regression
+
+# ## We load KernelFunctions and some other packages
+
+using KernelFunctions
+using LinearAlgebra
+using Distributions
+using Plots;
+default(; lw=2.0, legendfontsize=15.0);
+using Flux: Optimise
+using ForwardDiff
+using Random: seed!
+seed!(42)
+
+# ## Data Generation
+# We generated data in 1 dimension
+
+xmin = -3;
+xmax = 3; # Bounds of the data
+N = 50 # Number of samples
+x_train = rand(Uniform(xmin, xmax), N) # We sample 100 random samples
+σ = 0.1
+y_train = sinc.(x_train) + randn(N) * σ # We create a function and add some noise
+x_test = range(xmin - 0.1, xmax + 0.1; length=300)
+
+# Plot the data
+
+scatter(x_train, y_train; lab="data")
+plot!(x_test, sinc; lab="true function")
+
+# ## Kernel training
+# To train the kernel parameters via ForwardDiff.jl
+# we need to create a function creating a kernel from an array
+
+function kernelcall(θ)
+    return (exp(θ[1]) * SqExponentialKernel() + exp(θ[2]) * Matern32Kernel()) ∘
+           ScaleTransform(exp(θ[3]))
+end
+
+# From theory we know the prediction for a test set x given
+# the kernel parameters and normalization constant
+
+function f(x, x_train, y_train, θ)
+    k = kernelcall(θ[1:3])
+    return kernelmatrix(k, x, x_train) *
+           ((kernelmatrix(k, x_train) + exp(θ[4]) * I) \ y_train)
+end
+
+# We look how the prediction looks like
+# with starting parameters [1.0, 1.0, 1.0, 1.0] we get :
+
+ŷ = f(x_test, x_train, y_train, log.(ones(4)))
+scatter(x_train, y_train; lab="data")
+plot!(x_test, sinc; lab="true function")
+plot!(x_test, ŷ; lab="prediction")
+
+# We define the loss based on the L2 norm both
+# for the loss and the regularization
+
+function loss(θ)
+    ŷ = f(x_train, x_train, y_train, θ)
+    return sum(abs2, y_train - ŷ) + exp(θ[4]) * norm(ŷ)
+end
+
+# The loss with our starting point :
+
+loss(log.(ones(4)))
+
+# ## Training the model
+
+θ = log.([1.0, 0.1, 0.01, 0.001]) # Initial vector
+anim = Animation()
+opt = Optimise.ADAGrad(0.5)
+for i in 1:30
+    grads = ForwardDiff.gradient(loss, θ) # We compute the gradients given the kernel parameters and regularization
+    Optimise.update!(opt, θ, grads)
+    scatter(
+        x_train, y_train; lab="data", title="i = $(i), Loss = $(round(loss(θ), digits = 4))"
+    )
+    plot!(x_test, sinc; lab="true function")
+    plot!(x_test, f(x_test, x_train, y_train, θ); lab="Prediction", lw=3.0)
+    frame(anim)
+end
+gif(anim)