diff --git a/Swift/p008-Categorical-Cross-Entropy-Loss-applied.swift b/Swift/p008-Categorical-Cross-Entropy-Loss-applied.swift
new file mode 100644
index 0000000..27fefc6
--- /dev/null
+++ b/Swift/p008-Categorical-Cross-Entropy-Loss-applied.swift
@@ -0,0 +1,366 @@
+import GameKit
+import Foundation
+
+let (X, y) = NNfS.spiral_data(points: 100, classes: 3)
+
+protocol Layer {
+    var output: [[Double]] { get }
+    var weights: [[Double]] { get }
+    var biases: [Double] { get }
+    func forward(inputs:[[Double]])
+}
+
+class DenseLayer: Layer {
+    public var output: [[Double]]
+    public var weights: [[Double]]
+    public var biases: [Double]
+    
+    init(n_inputs: Int, n_neurons: Int) {
+        weights = (0..<n_inputs).map { _ in
+            (0..<n_neurons).map { _ in Double(NNfS.rd.nextUniform()) * 0.1 }
+        }
+        biases = Array(repeating: 0.0, count: n_neurons)
+        output = Array(repeating: [], count: n_neurons)
+    }
+    
+    func forward(inputs: [[Double]]) {
+        output = inputs.dot(weights)!.add(biases)!
+    }
+}
+
+protocol Activation {
+    var output: [[Double]] { get }
+    func forward(inputs:[[Double]])
+}
+
+class ReLU: Activation {
+    public var output: [[Double]]
+    
+    init() {
+        output = []
+    }
+
+    public func forward(inputs:[[Double]]) {
+        output = inputs.map{  row in row.map{ val in max(0, val) }  }
+    }
+}
+
+class SoftMax: Activation {
+    public var output: [[Double]]
+    
+    init() {
+        output = []
+    }
+
+    public func forward(inputs:[[Double]]) {
+        // exp(inputs - inputs.max)
+        let exp_values = inputs.map { row -> [Double] in
+            guard let max_value = row.max() else {
+                fatalError("invalid inputs")
+            }
+            return row.map { value in
+                exp(value - max_value)
+            }
+        }
+        
+        self.output = exp_values.map { row in row.div(row.reduce(0,+)) }
+    }
+}
+
+protocol Loss {
+    func forward(y_pred:[[Double]], y_true:[Int]) -> [Double]
+    func forward(y_pred:[[Double]], y_true:[[Int]]) -> [Double]
+}
+
+extension Loss {
+    func calculate(output:[[Double]], y:[Int]) -> Double {
+        let sample_losses = self.forward(y_pred: output, y_true: y)
+        let data_loss = sample_losses.mean
+        return data_loss
+    }
+    func calculate(output:[[Double]], y:[[Int]]) -> Double {
+        let sample_losses = self.forward(y_pred: output, y_true: y)
+        let data_loss = sample_losses.mean
+        return data_loss
+    }
+}
+
+class Loss_CategoricalCrossentropy: Loss {
+    public func forward(y_pred:[[Double]], y_true:[Int]) -> [Double] {
+        let samples = y_pred.count
+        let y_pred_clipped = y_pred.clip(to:1e-7...1-1e-7)
+
+        let correct_confidences = zip(Array(0...samples), y_true).map{ (x,y) in
+            y_pred_clipped[x][y]
+        }
+        
+        let negative_log_likelihoods = correct_confidences.map{ el in -log(el) }
+        return negative_log_likelihoods
+    }
+
+    public func forward(y_pred:[[Double]], y_true:[[Int]]) -> [Double] {
+        let y_pred_clipped = y_pred.clip(to:1e-7...1-1e-7)
+
+        let correct_confidences: [Double] = y_true
+            .map{ y_pred_clipped[$0.first!][$0.last!] }
+        
+        let negative_log_likelihoods = correct_confidences.map{ el in -log(el) }
+        return negative_log_likelihoods
+    }
+}
+
+let layer1 = DenseLayer(n_inputs: 2, n_neurons: 3)
+let activation1 = ReLU()
+
+let layer2 = DenseLayer(n_inputs: 3, n_neurons: 3)
+let activation2 = SoftMax()
+
+layer1.forward(inputs: X)
+activation1.forward(inputs: layer1.output)
+
+layer2.forward(inputs: activation1.output)
+activation2.forward(inputs: layer2.output)
+
+print(activation2.output.prefix(5))
+
+let loss_function = Loss_CategoricalCrossentropy()
+let loss = loss_function.calculate(output: activation2.output, y: y.map(Int.init))
+
+print("Loss:", loss)
+
+public class NNfS {
+    static public let rs = GKLinearCongruentialRandomSource(seed: 0)
+    static public let rd = GKGaussianDistribution(randomSource: rs, mean: 0, deviation: Float(UInt8.max))
+
+    // https://cs231n.github.io/neural-networks-case-study/
+    static public func spiral_data(points:Int, classes:Int) -> ([[Double]], [UInt8]) {
+        let height = points * classes
+        var X:[[Double]] = [[Double]](count: height, generating: { _ in [Double](repeating: 0.0, count: 2) })
+        var y:[UInt8] = [UInt8](repeating: 0, count: points*classes)
+        
+        for classNumber in 0..<classes {
+            let ix = points*classNumber..<points*(classNumber+1)
+
+            let r = Array(count: points, generating: { n in Double(n)/Double(points) }) // radius
+                        
+            let tl = Array(count: points, generating: { n -> Double in Double(n)/Double(points) * 4.0 + Double(classNumber) })
+            let tr = Array(count: points, generating: { _ in Double(rd.nextUniform()) * 0.2 })
+            let t = tl.add(tr)!
+                                    
+            let rSin:[Double] = r.mul(sin(radians: t.mul(2.5)))!
+            let rCos:[Double] = r.mul(cos(radians: t.mul(2.5)))!
+            let xSub = rSin.concatHorizontal(rCos)
+                        
+            X.replaceSubrange(  ix, with: xSub )
+            y.replaceSubrange(  ix, with: Array<UInt8>(repeating: UInt8(classNumber), count: ix.count) )
+        }
+
+        return (X, y)
+    }
+}
+
+extension Array {
+     /// Create a new Array whose values are generated by the given closure.
+     /// - Parameters:
+     ///     - count:            The number of elements to generate
+     ///     - elementGenerator: The closure that generates the elements.
+     ///                         The index into which the element will be
+     ///                         inserted is passed into the closure.
+    public init(count: Int, generating elementGenerator: (Int) -> Element) {
+        self = (0..<count).map(elementGenerator)
+    }
+}
+
+// The below replaces numpy in the pythonic examples =========
+
+extension Comparable {
+    func clamped(to limits: ClosedRange<Self>) -> Self {
+        return min(max(self, limits.lowerBound), limits.upperBound)
+    }
+}
+
+extension Array where Element: BinaryInteger {
+    var mean: Double {
+        if self.isEmpty {
+            return 0.0
+        } else {
+            let sum = self.reduce(0, +)
+            return Double(sum) / Double(self.count)
+        }
+    }
+}
+
+extension Array where Element: BinaryFloatingPoint {
+    var mean: Double {
+        if self.isEmpty {
+            return 0.0
+        } else {
+            let sum = self.reduce(0, +)
+            return Double(sum) / Double(self.count)
+        }
+    }
+}
+
+extension Array where Element == Double {
+    public func clip (to limits: ClosedRange<Double>) -> Self {
+        let r = self
+        return r.map{ row in
+            row.clamped(to: limits)
+        }
+    }
+    
+    public func dot (_ rval: [Double]) -> Double? {
+        if self.count != rval.count { return nil }
+        return zip(self, rval).reduce(0.0, { (sum, tuple) -> Double in
+             sum + tuple.0 * tuple.1
+        })
+    }
+
+    public func concatHorizontal(_ rval: [[Double]]) -> [[Double]] {
+        return self.T.concatHorizontal(rval)
+    }
+
+    public func concatHorizontal(_ rval: [Double]) -> [[Double]] {
+        return self.T.concatHorizontal(rval.T)
+    }
+
+    public var T: [[Double]] {
+        get {
+            return self.reduce(into: []){ $0.append([$1]) }
+        }
+    }
+
+    public func add(_ rval: [Double]) -> [Double]? {
+        if self.count != rval.count { return nil }
+
+        return zip(self, rval).map(+)
+    }
+
+    public func mul(_ rval: [Double]) -> [Double]? {
+        if self.count != rval.count { return nil }
+
+        return zip(self, rval).map(*)
+    }
+
+    public func div(_ rval: [Double]) -> [Double]? {
+        if self.count != rval.count { return nil }
+
+        return zip(self, rval).map(/)
+    }
+
+    public func mul(_ rval: Double) -> [Double] {
+        return self.map{ lval in lval * rval }
+    }
+
+    public func div(_ rval: Double) -> [Double] {
+        return self.map{ lval in lval / rval }
+    }
+}
+
+extension Array where Element == [Double] {
+    public func clip (to limits: ClosedRange<Double>) -> Self {
+        let r = self
+        return r.map{ row in
+            row.map { el in
+                el.clamped(to: limits)
+            }
+        }
+    }
+    
+    public func dot (_ rval: [Double]) -> [Double]? {
+        let rc = rval.count
+        if self.reduce(false, { $1.count != rc }) { return nil }
+
+        return self.map { (lval) -> Double in lval.dot(rval)! }
+    }
+
+    public func dot (_ rval: [[Double]]) -> [[Double]]? {
+        if(rval.count != self[0].count) { return nil }
+
+        let rt = rval.T
+        return zip(self.indices, self).map{ (index, row) -> [Double] in rt.dot(row)! }
+    }
+    
+    public func add(_ rval: [Double]) -> [[Double]]? {
+        if self[0].count != rval.count { return nil }
+
+        return self.map{ row in zip(row, rval).map(+) }
+    }
+
+    public var T: [[Double]] {
+        get {
+            var out: [[Double]] = []
+            let x = self.count
+            let y = self[0].count
+            let N = x > y ? x : y
+            let M = y < x ? y : x
+            for n in 0..<N {
+                for m in 0..<M {
+                    let a = x > y ? n : m
+                    let b = y < x ? m : n
+                    if out.count <= b {
+                        out.append([])
+                    }
+                    out[b].append(self[a][b])
+                }
+            }
+            return out
+        }
+    }
+
+    public func concatHorizontal(_ rval: [[Double]]) -> [[Double]] {
+        return zip(self.indices, self).map{ (index, row) in
+            var next = row
+            next.append(contentsOf: rval[index])
+            return next
+        }
+    }
+
+    public func elmul(_ rval: [[Double]]) -> [[Double]]? {
+        if rval.count != self.count || rval[0].count != self[0].count { return nil }
+
+        return zip(self, rval).map{ (lrow, rrow) in lrow.mul(rrow)! }
+    }
+
+    public func eldiv(_ rval: [[Double]]) -> [[Double]]? {
+        if rval.count != self.count || rval[0].count != self[0].count { return nil }
+
+        return zip(self, rval).map{ (lrow, rrow) in lrow.mul(rrow)! }
+    }
+
+    public func mul(_ rval: [Double]) -> [[Double]]? {
+        if rval.count != self[0].count { return nil }
+
+        return self.map{ row in row.mul(rval)! }
+    }
+
+    public func div(_ rval: [Double]) -> [[Double]]? {
+        if rval.count != self[0].count { return nil }
+
+        return self.map{ row in row.div(rval)! }
+    }
+
+    public func mul(_ rval: Double) -> [[Double]] {
+        return self.map{ row in row.mul(rval) }
+    }
+
+    public func div(_ rval: Double) -> [[Double]] {
+        return self.map{ row in row.div(rval) }
+    }
+}
+
+public func sin(radians: [[Double]]) -> [[Double]] {
+    return radians.map{ val in sin(radians: val) }
+}
+
+public func cos(radians: [[Double]]) -> [[Double]] {
+    return radians.map{ val in cos(radians: val) }
+}
+
+public func sin(radians: [Double]) -> [Double] {
+    return radians.map(sin)
+}
+
+public func cos(radians: [Double]) -> [Double] {
+    return radians.map(cos)
+}