diff --git a/src/machine_learning/loss_functions/mean_squared_error.h b/src/machine_learning/loss_functions/mean_squared_error.h
deleted file mode 100644
index 666b531b..00000000
--- a/src/machine_learning/loss_functions/mean_squared_error.h
+++ /dev/null
@@ -1,61 +0,0 @@
-#ifndef MEAN_SQUARED_ERROR_H
-#define MEAN_SQUARED_ERROR_H
-
-#ifdef __cplusplus
-#include <iostream>
-#include <algorithm>
-#include <vector>
-#include <cmath>
-#include <cassert>
-#endif
-
-
-namespace losses {
-  float mean_squared_error(std::vector<float> const& y, std::vector<float> const& y_hat) {
-    assert(y.size() == y_hat.size());
-    size_t n = y.size();
-    float mse = 0.0;
-    for(size_t i = 0; i<n; i++) {
-        mse += std::powf(y[i] - y_hat[i], 2);
-    }
-    return mse / float(n);
-  }
-
-  float root_mean_squared_error(std::vector<float> const& y, std::vector<float> const& y_hat) {
-    return std::sqrt(mean_squared_error(y, y_hat));
-  }
-    
-  float mean_absolute_error(std::vector<float> const& y, std::vector<float> const& y_hat) {
-    assert(y.size() == y_hat.size());
-    size_t n = y.size();
-    float mae = 0.0;
-    for(size_t i = 0; i<n; i++) {
-        mae += std::abs(y[i] - y_hat[i]);
-    }
-    return mae / float(n);
-  }
-
-  float binary_crossentropy_loss(std::vector<float> const& y, std::vector<float> const& y_hat) {
-    assert(y.size() == y_hat.size());
-    size_t n = y.size();
-    float bce = 0.0;
-    for(size_t i = 0; i<n; i++) {
-        bce += (y[i]*std::log(y_hat[i]) + (1-y[i])*std::log(1 - y_hat[i]));
-    }
-    return -(1/float(n))*bce;
-  }
-
-  float crossentropy_loss(std::vector<float> const& y, std::vector<float> const& y_hat) {
-    assert(y.size() == y_hat.size());
-    size_t n = y.size();
-    float ce = 0.0;
-    for(size_t i = 0; i<n; i++) {
-        ce += y[i]*std::log(y_hat[i]);
-    }
-    return -(1/float(n))*ce;
-  }
-}
-
-
-#endif
-