diff --git a/tensor_regression_layer.ipynb b/tensor_regression_layer.ipynb
index b5ba132..52725bd 100644
--- a/tensor_regression_layer.ipynb
+++ b/tensor_regression_layer.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# Tensor Regression Networks with ``TensorLy`` and ``PyTorch`` as a backend\n",
     "\n",
-    "In this notebook, we will show how to combine TensorLy and MXNet in to implement the tensor regression layer, as defined in **Tensor Contraction & Regression Networks**, _Jean Kossaifi, Zachary C. Lipton, Aran Khanna, Tommaso Furlanello and Anima Anandkumar_, [ArXiV pre-publication](https://arxiv.org/abs/1707.08308).\n",
+    "In this notebook, we will show how to combine TensorLy and PyTorch in to implement the tensor regression layer, as defined in **Tensor Contraction & Regression Networks**, _Jean Kossaifi, Zachary C. Lipton, Aran Khanna, Tommaso Furlanello and Anima Anandkumar_, [ArXiV pre-publication](https://arxiv.org/abs/1707.08308).\n",
     "\n",
     "\n",
     "Specifically, we use [TensorLy](http://tensorly.org/dev/index.html) for the tensor operations, with the [PyTorch](http://pytorch.org/) backend.\n",
@@ -124,7 +124,7 @@
     "        return F.log_softmax(x)\n",
     "```\n",
     "\n",
-    "In this notebook, we will demonstrate how to implement easily the TRL using TensorLy and MXNet."
+    "In this notebook, we will demonstrate how to implement easily the TRL using TensorLy and PyTorch."
    ]
   },
   {
@@ -142,16 +142,13 @@
    "source": [
     "import torch\n",
     "import torch.nn as nn\n",
-    "from torch.autograd import Variable\n",
     "import torch.optim as optim\n",
     "from torchvision import datasets, transforms\n",
     "import torch.nn.functional as F\n",
     "\n",
     "import numpy as np\n",
     "\n",
-    "import tensorly as tl\n",
-    "from tensorly.tenalg import inner\n",
-    "from tensorly.random import check_random_state"
+    "from tensorly.tenalg import inner"
    ]
   },
   {
@@ -187,7 +184,7 @@
     "batch_size = 16\n",
     "device = 'cuda:0'\n",
     "# to run on CPU, uncomment the following line:\n",
-    "device = 'cpu'"
+    "# device = 'cpu'"
    ]
   },
   {
@@ -274,7 +271,7 @@
     "    def penalty(self, order=2):\n",
     "        penalty = tl.norm(self.core, order)\n",
     "        for f in self.factors:\n",
-    "            penatly = penalty + tl.norm(f, order)\n",
+    "            penalty = penalty + tl.norm(f, order)\n",
     "        return penalty\n"
    ]
   },
@@ -395,7 +392,7 @@
    ],
    "source": [
     "n_epoch = 5 # Number of epochs\n",
-    "regularizer = 0.001\n",
+    "regularizer = 0.0001\n",
     "\n",
     "model = model.to(device)\n",
     "\n",
diff --git a/tt-compression.ipynb b/tt-compression.ipynb
index 1893ad6..24f7ca9 100644
--- a/tt-compression.ipynb
+++ b/tt-compression.ipynb
@@ -32,8 +32,7 @@
     "\n",
     "import numpy as np\n",
     "\n",
-    "import tensorly as tl\n",
-    "from tensorly.random import check_random_state"
+    "import tensorly as tl"
    ]
   },
   {