diff --git a/Manual/Manual_How_to_create_user_minibatch_sources.ipynb b/Manual/Manual_How_to_create_user_minibatch_sources.ipynb
index 5788f0768..c07916927 100644
--- a/Manual/Manual_How_to_create_user_minibatch_sources.ipynb
+++ b/Manual/Manual_How_to_create_user_minibatch_sources.ipynb
@@ -369,7 +369,7 @@
     "        return {'next_seq_idx': self.next_seq_idx}\n",
     "    \n",
     "    def restore_from_checkpoint(self, state):\n",
-    "        self.next_seq_idx = state['next_seq_idx']\n"
+    "        self.next_seq_idx = state['next_seq_idx']"
    ]
   },
   {
@@ -390,7 +390,9 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -419,7 +421,7 @@
     "loss = C.cross_entropy_with_softmax(z, label)\n",
     "errs = C.classification_error(z, label)\n",
     "local_learner = C.sgd(z.parameters, \n",
-    "                      C.learning_rate_schedule(0.5, unit = C.UnitType.sample))\n",
+    "                      C.learning_parameter_schedule_per_sample(0.5))\n",
     "dist_learner = C.distributed.data_parallel_distributed_learner(local_learner)\n",
     "# and train\n",
     "trainer = C.Trainer(z, (loss, errs), \n",
@@ -476,5 +478,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 0
 }
diff --git a/Manual/Manual_How_to_feed_data.ipynb b/Manual/Manual_How_to_feed_data.ipynb
index 6f2663d86..b79e12dfb 100644
--- a/Manual/Manual_How_to_feed_data.ipynb
+++ b/Manual/Manual_How_to_feed_data.ipynb
@@ -67,7 +67,9 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -120,7 +122,9 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -149,7 +153,9 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -173,7 +179,7 @@
    ],
    "source": [
     "learner = C.sgd(model.parameters,\n",
-    "                C.learning_rate_schedule(0.1, C.UnitType.minibatch))\n",
+    "                C.learning_parameter_schedule(0.1))\n",
     "progress_writer = C.logging.ProgressPrinter(0)\n",
     "\n",
     "train_summary = loss.train((X_train_lr, Y_train_lr), parameter_learners=[learner],\n",
@@ -190,7 +196,9 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -271,7 +279,9 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -348,7 +358,7 @@
     "z = model(x/255.0) #scale the input to 0-1 range\n",
     "loss = C.cross_entropy_with_softmax(z, y)\n",
     "learner = C.sgd(z.parameters,\n",
-    "                C.learning_rate_schedule(0.05, C.UnitType.minibatch))"
+    "                C.learning_parameter_schedule(0.05))"
    ]
   },
   {
@@ -365,7 +375,9 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -423,7 +435,9 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -453,7 +467,7 @@
     "z1 = model(x/255.0) #scale the input to 0-1 range\n",
     "loss = C.cross_entropy_with_softmax(z1, y)\n",
     "learner = C.sgd(z1.parameters,\n",
-    "                C.learning_rate_schedule(0.05, C.UnitType.minibatch))\n",
+    "                C.learning_parameter_schedule(0.05))\n",
     "\n",
     "num_minibatches_to_train = (num_samples_per_sweep * num_sweeps_to_train_with) / minibatch_size\n",
     "\n",
@@ -613,7 +627,9 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -664,6 +680,7 @@
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
+    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
diff --git a/Manual/Manual_How_to_train_using_declarative_and_imperative_API.ipynb b/Manual/Manual_How_to_train_using_declarative_and_imperative_API.ipynb
index 4809a84f5..354637177 100644
--- a/Manual/Manual_How_to_train_using_declarative_and_imperative_API.ipynb
+++ b/Manual/Manual_How_to_train_using_declarative_and_imperative_API.ipynb
@@ -67,7 +67,9 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -130,7 +132,7 @@
     "\n",
     "# Create a learner and a trainer and a progress writer to \n",
     "# output current progress\n",
-    "learner = cntk.sgd(model.parameters, cntk.learning_rate_schedule(0.1, cntk.UnitType.sample))\n",
+    "learner = cntk.sgd(model.parameters, cntk.learning_parameter_schedule_per_sample(0.1))\n",
     "trainer = cntk.train.Trainer(z, (loss, loss), learner, ProgressPrinter(freq=10))\n",
     "\n",
     "# Now let's create a minibatch source for our input file\n",
@@ -186,7 +188,9 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -231,7 +235,7 @@
     "z = model(features)\n",
     "loss = cntk.squared_error(z, label)\n",
     "\n",
-    "learner = cntk.sgd(model.parameters, cntk.learning_rate_schedule(0.1, cntk.UnitType.sample))\n",
+    "learner = cntk.sgd(model.parameters, cntk.learning_parameter_schedule_per_sample(0.1))\n",
     "trainer = cntk.train.Trainer(z, (loss, loss), learner, ProgressPrinter(freq=10))\n",
     "\n",
     "# Try to restore if the checkpoint exists\n",
@@ -281,7 +285,9 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -330,7 +336,7 @@
     "\n",
     "# Make sure the learner is distributed\n",
     "distributed_learner = cntk.distributed.data_parallel_distributed_learner(\n",
-    "    cntk.sgd(model.parameters, cntk.learning_rate_schedule(0.1, cntk.UnitType.sample)))\n",
+    "    cntk.sgd(model.parameters, cntk.learning_parameter_schedule_per_sample(0.1)))\n",
     "trainer = cntk.train.Trainer(z, (loss, loss), distributed_learner, ProgressPrinter(freq=10))\n",
     "\n",
     "if os.path.exists(checkpoint):\n",
@@ -385,6 +391,7 @@
    "cell_type": "code",
    "execution_count": 33,
    "metadata": {
+    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -441,7 +448,7 @@
     "\n",
     "criterion = criterion_factory(features, label)\n",
     "learner = cntk.distributed.data_parallel_distributed_learner(cntk.sgd(model.parameters, \n",
-    "                                                                      cntk.learning_rate_schedule(0.1, cntk.UnitType.sample)))\n",
+    "                                                                      cntk.learning_parameter_schedule_per_sample(0.1)))\n",
     "progress_writer = cntk.logging.ProgressPrinter(freq=10)\n",
     "checkpoint_config = cntk.CheckpointConfig(filename=checkpoint, frequency=checkpoint_frequency)\n",
     "test_config = cntk.TestConfig(test_mb_source)\n",