diff --git a/doc/src/week39/codes/adagrad.py b/doc/src/week39/codes/adagrad.py
new file mode 100644
index 000000000..3e9234c1d
--- /dev/null
+++ b/doc/src/week39/codes/adagrad.py
@@ -0,0 +1,73 @@
+from math import sqrt
+from numpy import asarray
+from numpy import arange
+from numpy.random import rand
+from numpy.random import seed
+from numpy import meshgrid
+from matplotlib import pyplot
+from mpl_toolkits.mplot3d import Axes3D
+ 
+# objective function
+def objective(x, y):
+	return x**2.0 + y**2.0
+ 
+# derivative of objective function
+def derivative(x, y):
+	return asarray([x * 2.0, y * 2.0])
+ 
+# gradient descent algorithm with adagrad
+def adagrad(objective, derivative, bounds, n_iter, step_size):
+	# track all solutions
+	solutions = list()
+	# generate an initial point
+	solution = bounds[:, 0] + rand(len(bounds)) * (bounds[:, 1] - bounds[:, 0])
+	# list of the sum square gradients for each variable
+	sq_grad_sums = [0.0 for _ in range(bounds.shape[0])]
+	# run the gradient descent
+	for it in range(n_iter):
+		# calculate gradient
+		gradient = derivative(solution[0], solution[1])
+		# update the sum of the squared partial derivatives
+		for i in range(gradient.shape[0]):
+			sq_grad_sums[i] += gradient[i]**2.0
+		# build solution
+		new_solution = list()
+		for i in range(solution.shape[0]):
+			# calculate the learning rate for this variable
+			alpha = step_size / (1e-8 + sqrt(sq_grad_sums[i]))
+			# calculate the new position in this variable
+			value = solution[i] - alpha * gradient[i]
+			new_solution.append(value)
+		# store the new solution
+		solution = asarray(new_solution)
+		solutions.append(solution)
+		# evaluate candidate point
+		solution_eval = objective(solution[0], solution[1])
+		# report progress
+		print('>%d f(%s) = %.5f' % (it, solution, solution_eval))
+	return solutions
+ 
+# seed the pseudo random number generator
+seed(1)
+# define range for input
+bounds = asarray([[-1.0, 1.0], [-1.0, 1.0]])
+# define the total iterations
+n_iter = 50
+# define the step size
+step_size = 0.1
+# perform the gradient descent search
+solutions = adagrad(objective, derivative, bounds, n_iter, step_size)
+# sample input range uniformly at 0.1 increments
+xaxis = arange(bounds[0,0], bounds[0,1], 0.1)
+yaxis = arange(bounds[1,0], bounds[1,1], 0.1)
+# create a mesh from the axis
+x, y = meshgrid(xaxis, yaxis)
+# compute targets
+results = objective(x, y)
+# create a filled contour plot with 50 levels and jet color scheme
+pyplot.contourf(x, y, results, levels=50, cmap='jet')
+# plot the sample as black circles
+solutions = asarray(solutions)
+pyplot.plot(solutions[:, 0], solutions[:, 1], '.-', color='w')
+# show the plot
+pyplot.show()
diff --git a/doc/src/week39/codes/adam.py b/doc/src/week39/codes/adam.py
new file mode 100644
index 000000000..a42c6de64
--- /dev/null
+++ b/doc/src/week39/codes/adam.py
@@ -0,0 +1,78 @@
+from math import sqrt
+from numpy import asarray
+from numpy import arange
+from numpy.random import rand
+from numpy.random import seed
+from numpy import meshgrid
+from matplotlib import pyplot
+from mpl_toolkits.mplot3d import Axes3D
+ 
+# objective function
+def objective(x, y):
+	return x**2.0 + y**2.0
+ 
+# derivative of objective function
+def derivative(x, y):
+	return asarray([x * 2.0, y * 2.0])
+ 
+# gradient descent algorithm with adam
+def adam(objective, derivative, bounds, n_iter, alpha, beta1, beta2, eps=1e-8):
+	solutions = list()
+	# generate an initial point
+	x = bounds[:, 0] + rand(len(bounds)) * (bounds[:, 1] - bounds[:, 0])
+	score = objective(x[0], x[1])
+	# initialize first and second moments
+	m = [0.0 for _ in range(bounds.shape[0])]
+	v = [0.0 for _ in range(bounds.shape[0])]
+	# run the gradient descent updates
+	for t in range(n_iter):
+		# calculate gradient g(t)
+		g = derivative(x[0], x[1])
+		# build a solution one variable at a time
+		for i in range(bounds.shape[0]):
+			# m(t) = beta1 * m(t-1) + (1 - beta1) * g(t)
+			m[i] = beta1 * m[i] + (1.0 - beta1) * g[i]
+			# v(t) = beta2 * v(t-1) + (1 - beta2) * g(t)^2
+			v[i] = beta2 * v[i] + (1.0 - beta2) * g[i]**2
+			# mhat(t) = m(t) / (1 - beta1(t))
+			mhat = m[i] / (1.0 - beta1**(t+1))
+			# vhat(t) = v(t) / (1 - beta2(t))
+			vhat = v[i] / (1.0 - beta2**(t+1))
+			# x(t) = x(t-1) - alpha * mhat(t) / (sqrt(vhat(t)) + ep)
+			x[i] = x[i] - alpha * mhat / (sqrt(vhat) + eps)
+		# evaluate candidate point
+		score = objective(x[0], x[1])
+		# keep track of solutions
+		solutions.append(x.copy())
+		# report progress
+		print('>%d f(%s) = %.5f' % (t, x, score))
+	return solutions
+ 
+# seed the pseudo random number generator
+seed(1)
+# define range for input
+bounds = asarray([[-1.0, 1.0], [-1.0, 1.0]])
+# define the total iterations
+n_iter = 60
+# steps size
+alpha = 0.02
+# factor for average gradient
+beta1 = 0.8
+# factor for average squared gradient
+beta2 = 0.999
+# perform the gradient descent search with adam
+solutions = adam(objective, derivative, bounds, n_iter, alpha, beta1, beta2)
+# sample input range uniformly at 0.1 increments
+xaxis = arange(bounds[0,0], bounds[0,1], 0.1)
+yaxis = arange(bounds[1,0], bounds[1,1], 0.1)
+# create a mesh from the axis
+x, y = meshgrid(xaxis, yaxis)
+# compute targets
+results = objective(x, y)
+# create a filled contour plot with 50 levels and jet color scheme
+pyplot.contourf(x, y, results, levels=50, cmap='jet')
+# plot the sample as black circles
+solutions = asarray(solutions)
+pyplot.plot(solutions[:, 0], solutions[:, 1], '.-', color='w')
+# show the plot
+pyplot.show()
diff --git a/doc/src/week39/codes/rmsprop.py b/doc/src/week39/codes/rmsprop.py
new file mode 100644
index 000000000..3158902cb
--- /dev/null
+++ b/doc/src/week39/codes/rmsprop.py
@@ -0,0 +1,78 @@
+from math import sqrt
+from numpy import asarray
+from numpy import arange
+from numpy.random import rand
+from numpy.random import seed
+from numpy import meshgrid
+from matplotlib import pyplot
+from mpl_toolkits.mplot3d import Axes3D
+ 
+# objective function
+def objective(x, y):
+	return x**2.0 + y**2.0
+ 
+# derivative of objective function
+def derivative(x, y):
+	return asarray([x * 2.0, y * 2.0])
+ 
+# gradient descent algorithm with rmsprop
+def rmsprop(objective, derivative, bounds, n_iter, step_size, rho):
+	# track all solutions
+	solutions = list()
+	# generate an initial point
+	solution = bounds[:, 0] + rand(len(bounds)) * (bounds[:, 1] - bounds[:, 0])
+	# list of the average square gradients for each variable
+	sq_grad_avg = [0.0 for _ in range(bounds.shape[0])]
+	# run the gradient descent
+	for it in range(n_iter):
+		# calculate gradient
+		gradient = derivative(solution[0], solution[1])
+		# update the average of the squared partial derivatives
+		for i in range(gradient.shape[0]):
+			# calculate the squared gradient
+			sg = gradient[i]**2.0
+			# update the moving average of the squared gradient
+			sq_grad_avg[i] = (sq_grad_avg[i] * rho) + (sg * (1.0-rho))
+		# build solution
+		new_solution = list()
+		for i in range(solution.shape[0]):
+			# calculate the learning rate for this variable
+			alpha = step_size / (1e-8 + sqrt(sq_grad_avg[i]))
+			# calculate the new position in this variable
+			value = solution[i] - alpha * gradient[i]
+			new_solution.append(value)
+		# store the new solution
+		solution = asarray(new_solution)
+		solutions.append(solution)
+		# evaluate candidate point
+		solution_eval = objective(solution[0], solution[1])
+		# report progress
+		print('>%d f(%s) = %.5f' % (it, solution, solution_eval))
+	return solutions
+ 
+# seed the pseudo random number generator
+seed(1)
+# define range for input
+bounds = asarray([[-1.0, 1.0], [-1.0, 1.0]])
+# define the total iterations
+n_iter = 50
+# define the step size
+step_size = 0.01
+# momentum for rmsprop
+rho = 0.99
+# perform the gradient descent search with rmsprop
+solutions = rmsprop(objective, derivative, bounds, n_iter, step_size, rho)
+# sample input range uniformly at 0.1 increments
+xaxis = arange(bounds[0,0], bounds[0,1], 0.1)
+yaxis = arange(bounds[1,0], bounds[1,1], 0.1)
+# create a mesh from the axis
+x, y = meshgrid(xaxis, yaxis)
+# compute targets
+results = objective(x, y)
+# create a filled contour plot with 50 levels and jet color scheme
+pyplot.contourf(x, y, results, levels=50, cmap='jet')
+# plot the sample as black circles
+solutions = asarray(solutions)
+pyplot.plot(solutions[:, 0], solutions[:, 1], '.-', color='w')
+# show the plot
+pyplot.show()