diff --git a/Descensogradientecomp.py b/Descensogradientecomp.py
new file mode 100644
index 0000000..c21bf14
--- /dev/null
+++ b/Descensogradientecomp.py
@@ -0,0 +1,131 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+###############################
+#Datos originales
+###############################
+X = 2 * np.random.rand(100, 1)
+y = 4 + 3 * X + np.random.randn(100,1)
+
+###############################
+
+eta = 0.1
+n_iterations = 1000
+m = 100
+X_b = np.c_[np.ones((100, 1)), X]  # add x0 = 1 to each instance
+theta = np.random.randn(2,1)
+X_new = np.array([[0], [2]])
+X_new_b = np.c_[np.ones((2, 1)), X_new]  # add x0 = 1 to each instance
+
+for iteration in range(n_iterations):
+    gradients = 2/m * X_b.T.dot(X_b.dot(theta) - y)
+    theta = theta - eta * gradients
+
+theta_path_bgd = []
+
+def plot_gradient_descent(theta, eta, theta_path=None):
+    m = len(X_b)
+    plt.plot(X, y, "b.")
+    n_iterations = 1000
+    for iteration in range(n_iterations):
+        if iteration < 10:
+            y_predict = X_new_b.dot(theta)
+            style = "b-" if iteration > 0 else "r--"
+            plt.plot(X_new, y_predict, style)
+        gradients = 2/m * X_b.T.dot(X_b.dot(theta) - y)
+        theta = theta - eta * gradients
+        if theta_path is not None:
+            theta_path.append(theta)
+    plt.xlabel("$x_1$", fontsize=18)
+    plt.axis([0, 2, 0, 15])
+    plt.title(r"$\eta = {}$".format(eta), fontsize=16)
+
+np.random.seed(42)
+theta = np.random.randn(2,1)  # random initialization
+
+plt.figure(figsize=(10,4))
+plt.subplot(131); plot_gradient_descent(theta, eta=0.02)
+plt.ylabel("$y$", rotation=0, fontsize=18)
+plt.subplot(132); plot_gradient_descent(theta, eta=0.1, theta_path=theta_path_bgd)
+plt.subplot(133); plot_gradient_descent(theta, eta=0.5)
+
+plt.show()
+
+theta_path_sgd = []
+m = len(X_b)
+np.random.seed(42)
+
+n_epochs = 50
+t0, t1 = 5, 50  # learning schedule hyperparameters
+
+def learning_schedule(t):
+    return t0 / (t + t1)
+
+theta = np.random.randn(2,1)  # random initialization
+
+for epoch in range(n_epochs):
+    for i in range(m):
+        if epoch == 0 and i < 20:                    # not shown in the book
+            y_predict = X_new_b.dot(theta)           # not shown
+            style = "b-" if i > 0 else "r--"         # not shown
+            plt.plot(X_new, y_predict, style)        # not shown
+        random_index = np.random.randint(m)
+        xi = X_b[random_index:random_index+1]
+        yi = y[random_index:random_index+1]
+        gradients = 2 * xi.T.dot(xi.dot(theta) - yi)
+        eta = learning_schedule(epoch * m + i)
+        theta = theta - eta * gradients
+        theta_path_sgd.append(theta)                 # not shown
+
+plt.plot(X, y, "b.")                                 # not shown
+plt.xlabel("$x_1$", fontsize=18)                     # not shown
+plt.ylabel("$y$", rotation=0, fontsize=18)           # not shown
+plt.axis([0, 2, 0, 15])                              # not shown
+plt.show()                                           # not shown
+
+theta_path_mgd = []
+
+n_iterations = 50
+minibatch_size = 20
+
+np.random.seed(42)
+theta = np.random.randn(2,1)  # random initialization
+
+t0, t1 = 200, 1000
+def learning_schedule(t):
+    return t0 / (t + t1)
+
+t = 0
+for epoch in range(n_iterations):
+    shuffled_indices = np.random.permutation(m)
+    X_b_shuffled = X_b[shuffled_indices]
+    y_shuffled = y[shuffled_indices]
+    for i in range(0, m, minibatch_size):
+        t += 1
+        xi = X_b_shuffled[i:i+minibatch_size]
+        yi = y_shuffled[i:i+minibatch_size]
+        gradients = 2/minibatch_size * xi.T.dot(xi.dot(theta) - yi)
+        eta = learning_schedule(t)
+        theta = theta - eta * gradients
+        theta_path_mgd.append(theta)
+
+
+
+
+theta_path_bgd = np.array(theta_path_bgd)
+theta_path_sgd = np.array(theta_path_sgd)
+theta_path_mgd = np.array(theta_path_mgd)
+
+
+
+plt.figure(figsize=(7,4))
+plt.plot(theta_path_sgd[:, 0], theta_path_sgd[:, 1], "r-s", linewidth=1, label="Stochastic")
+plt.plot(theta_path_mgd[:, 0], theta_path_mgd[:, 1], "g-+", linewidth=2, label="Mini-batch")
+plt.plot(theta_path_bgd[:, 0], theta_path_bgd[:, 1], "b-o", linewidth=3, label="Batch")
+plt.legend(loc="upper left", fontsize=16)
+plt.xlabel(r"$\theta_0$", fontsize=20)
+plt.ylabel(r"$\theta_1$   ", fontsize=20, rotation=0)
+plt.axis([2.5, 4.5, 2.3, 3.9])
+plt.show()
+