diff --git a/C++/p003-Dot-Product.cpp b/C++/p003-Dot-Product.cpp
index f159b8f..4041124 100644
--- a/C++/p003-Dot-Product.cpp
+++ b/C++/p003-Dot-Product.cpp
@@ -3,53 +3,40 @@
  * 
  * Associated tutorial https://www.youtube.com/watch?v=tMrbN67U9d4
  */
-#include <algorithm>
 #include <iostream>
-#include <iterator>
 #include <numeric>
-#include <tuple>
 #include <vector>
 
-// Simple implementation of built-in python zip
-template<typename T, typename P>
-std::vector<std::tuple<T, P>> zip(typename std::vector<T>::iterator firstArrayBegin, 
-			 typename std::vector<P>::iterator secondArrayBegin,
-			 typename std::vector<P>::const_iterator secondArrayEnd)
+template<typename T>
+void print_vec(const std::vector<T>& vec, const std::string title)
 {
-	const auto size = std::distance(static_cast<decltype(secondArrayEnd)>(secondArrayBegin), secondArrayEnd);
-	std::vector<std::tuple<T, P>> zipped;
-	zipped.reserve(size);
-	while (secondArrayBegin != secondArrayEnd) {
-		zipped.emplace_back(*firstArrayBegin++, *secondArrayBegin++);
-	}
-	return zipped;
+    std::cout << "\n----- " << title << " -----\n";
+    for (int i{}; i != vec.size(); ++i)
+        std::cout << vec[i] << (!((i +  1) % 5) ? "\n" : "  ");
+    std::cout << "\n----- " << title << " -----\n";
 }
 
+
 int main() {
 	// Inputs
-	std::vector<double> inputs{1.0, 2.0, 3.0, 2.5};
-	std::vector<std::vector<double>> weights{{.2, 0.8, -0.5, 1.0},
-		{0.5, -0.91, 0.26, -0.5},
-		{-0.26, -0.27, 0.17, 0.87}};
-	std::vector<double> biases{2.0, 3.0, 0.5};
+	std::vector<double> inputs{ 1.0, 2.0, 3.0, 2.5 };
+	std::vector<std::vector<double>> weights{
+		{ 0.2,    0.8,  -0.5,  1.0  },
+		{ 0.5,   -0.91, 0.26, -0.5  },
+		{ -0.26, -0.27, 0.17,  0.87 }
+	};
+	std::vector<double> biases{ 2.0, 3.0, 0.5 };
 	
-	// Zipping
-	auto zipped_weights_and_biases = 
-		zip<decltype(weights)::value_type, double>(weights.begin(), biases.begin(), biases.end());
 	
 	// Calculating
-	std::vector<double> outputs;
-	outputs.reserve(std::size(zipped_weights_and_biases));
-	std::transform(std::begin(zipped_weights_and_biases), std::end(zipped_weights_and_biases), 
-		    std::back_inserter(outputs),  [&inputs](const auto &weightAndBias) {
-	     return std::inner_product(std::begin(std::get<0>(weightAndBias)), 
-			     std::end(std::get<0>(weightAndBias)), 
-			     std::begin(inputs), 0.0)+std::get<1>(weightAndBias); 
-	}); 
+	std::vector<double> outputs(biases.size());
+	
+	// inner product of inputs and weights on every iterate, also init value is biases[i].
+	for (size_t i{}; i != weights.size(); ++i)
+		outputs[i] = std::inner_product(inputs.begin(), inputs.end(), weights[i].begin(), biases[i]);
 
 	// Ouputing
-	std::cout << '[';
-	std::copy(outputs.begin(), outputs.end(), std::ostream_iterator<double>(std::cout, " "));
-	std::cout << ']';
+	print_vec(outputs, "Output");
+	
 	return 0;
 }