typo corrections

doc/pub/week39/ipynb/week39.ipynb

@@ -1031,15 +1031,15 @@
     "from mpl_toolkits.mplot3d import axes3d\n",
     "\n",
     "def f(x):\n",
-    "    return 0.5*x[0]**2 + 2.5*x[1]**2\n",
+    "    return x[0]**2 + 3.0*x[1]**2\n",
     "\n",
     "def df(x):\n",
-    "    return np.array([x[0], 5*x[1]])\n",
+    "    return np.array([2*x[0], 6*x[1]])\n",
     "\n",
     "fig = pt.figure()\n",
     "ax = fig.gca(projection=\"3d\")\n",
     "\n",
-    "xmesh, ymesh = np.mgrid[-2:2:50j,-2:2:50j]\n",
+    "xmesh, ymesh = np.mgrid[-3:3:50j,-3:3:50j]\n",
     "fmesh = f(np.array([xmesh, ymesh]))\n",
     "ax.plot_surface(xmesh, ymesh, fmesh)"
    ]
@@ -1136,6 +1136,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "Note that we did only one iteration here. We can easily add more using our previous guesses.\n",
+    "\n",
     "## Conjugate gradient method\n",
     "In the CG method we define so-called conjugate directions and two vectors \n",
     "$\\boldsymbol{s}$ and $\\boldsymbol{t}$\n",
@@ -1695,7 +1697,7 @@
     "H = (2.0/n)* X.T @ X\n",
     "# Get the eigenvalues\n",
     "EigValues, EigVectors = np.linalg.eig(H)\n",
-    "print(EigValues)\n",
+    "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n",
     "\n",
     "beta_linreg = np.linalg.inv(X.T @ X) @ X.T @ y\n",
     "print(beta_linreg)\n",