diff --git a/brainstorm/training/steppers.py b/brainstorm/training/steppers.py
index 286e547..13070ed 100644
--- a/brainstorm/training/steppers.py
+++ b/brainstorm/training/steppers.py
@@ -143,3 +143,62 @@ def run(self):
         self.net.handler.mult_add_st(-learning_rate,
                                      self.net.buffer.gradients,
                                      self.net.buffer.parameters)
+                                     
+class AdamStepper(TrainingStepper):
+    """
+    Adam optimizer.
+    Decay rate lamb (lambda) decays beta1, thus slowly increasing momentum.
+    For more detailed information see "Adam: A Method for Stochastic Optimization" by Kingma and Ba.
+    """
+    __undescribed__ = {'m_0', 'v_0'}
+
+    def __init__(self, alpha=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8, lamb=1-1e-8):
+        super(AdamStepper, self).__init__()
+        self.m_0 = None
+        self.v_0 = None
+        self.time_step = None
+        self.alpha = alpha
+        self.beta1 = beta1
+        self.beta2 = beta2
+        self.epsilon = epsilon
+        self.lamb = lamb
+
+    def start(self, net):
+        super(AdamStepper, self).start(net)
+        self.m_0 = net.handler.zeros(net.buffer.parameters.shape)
+        self.v_0 = net.handler.zeros(net.buffer.parameters.shape)
+        self.time_step = 0
+
+    def run(self):
+        self.time_step += 1
+        self.beta1 *= self.lamb
+        t = self.time_step
+        learning_rate = self.alpha
+        self.net.forward_pass(training_pass=True)
+        self.net.backward_pass()
+
+        gradient = self.net.buffer.gradients
+        temp = self.net.handler.allocate(gradient.shape)
+        temp_m0 = self.net.handler.allocate(self.m_0.shape)
+        temp_v0 = self.net.handler.allocate(self.v_0.shape)
+
+        # m_t <- beta_1*m_{t-1} + (1-beta1) *gradient
+        self.net.handler.mult_st(self.beta1, self.m_0, out=self.m_0)
+        self.net.handler.mult_add_st(1.0-self.beta1, gradient, out=self.m_0)
+        # v_t <- beta_2*v_{t-1} + (1-beta2) *gradient^2
+        self.net.handler.mult_st(self.beta2, self.v_0, out=self.v_0)
+        self.net.handler.mult_tt(gradient, gradient, temp)  # gradient^2
+        self.net.handler.mult_add_st(1.0-self.beta2, temp, out=self.v_0)
+        # m_hat_t <- m_t/(1-beta1^t)
+        self.net.handler.mult_st(1.0/(1.0-pow(self.beta1, t)), self.m_0, out=temp_m0)
+        # v_hat_t <- v_t/(1-beta2^t)
+        self.net.handler.mult_st(1.0/(1.0-pow(self.beta2, t)), self.v_0, out=temp_v0)
+
+        self.net.handler.sqrt_t(temp_v0, temp_v0)
+        self.net.handler.add_st(self.epsilon, temp_v0, out=temp_v0)
+
+        self.net.handler.mult_st(learning_rate, temp_m0, out=temp_m0)
+
+        self.net.handler.divide_tt(temp_m0, temp_v0, temp)
+
+        self.net.handler.subtract_tt(self.net.buffer.parameters, temp, out=self.net.buffer.parameters)