diff --git a/My_Experiments/PyTorch_Course/00_pytorch_fundamentals.ipynb b/My_Experiments/PyTorch_Course/00_pytorch_fundamentals.ipynb
index 3268457..81f77f7 100644
--- a/My_Experiments/PyTorch_Course/00_pytorch_fundamentals.ipynb
+++ b/My_Experiments/PyTorch_Course/00_pytorch_fundamentals.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 63,
    "id": "4138ee2e",
    "metadata": {},
    "outputs": [
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 64,
    "id": "fa46b5f3",
    "metadata": {},
    "outputs": [
@@ -43,7 +43,7 @@
        "tensor(7)"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 65,
    "id": "80146c19",
    "metadata": {},
    "outputs": [
@@ -66,7 +66,7 @@
        "0"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -77,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 66,
    "id": "8ebd6387",
    "metadata": {},
    "outputs": [
@@ -87,7 +87,7 @@
        "7"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -98,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 67,
    "id": "975ba0ba",
    "metadata": {},
    "outputs": [
@@ -108,7 +108,7 @@
        "tensor([7, 7])"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -120,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 68,
    "id": "adc95f24",
    "metadata": {},
    "outputs": [
@@ -130,7 +130,7 @@
        "1"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -141,7 +141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 69,
    "id": "1364d5be",
    "metadata": {},
    "outputs": [
@@ -151,7 +151,7 @@
        "torch.Size([2])"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -162,7 +162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 70,
    "id": "3d811521",
    "metadata": {},
    "outputs": [
@@ -173,7 +173,7 @@
        "        [ 9, 10]])"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -186,7 +186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 71,
    "id": "e9acf917",
    "metadata": {},
    "outputs": [
@@ -196,7 +196,7 @@
        "2"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -207,7 +207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 72,
    "id": "40bd74ac",
    "metadata": {},
    "outputs": [
@@ -217,7 +217,7 @@
        "tensor([7, 8])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -228,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 73,
    "id": "8093f799",
    "metadata": {},
    "outputs": [
@@ -238,7 +238,7 @@
        "tensor([ 9, 10])"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -249,7 +249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 74,
    "id": "85b1d000",
    "metadata": {},
    "outputs": [
@@ -259,7 +259,7 @@
        "torch.Size([2, 2])"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -270,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 75,
    "id": "2ff8b7c4",
    "metadata": {},
    "outputs": [],
@@ -290,7 +290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 76,
    "id": "1e12a399",
    "metadata": {},
    "outputs": [
@@ -300,7 +300,7 @@
        "3"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -311,7 +311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 77,
    "id": "32e5193a",
    "metadata": {},
    "outputs": [
@@ -321,7 +321,7 @@
        "torch.Size([3, 3, 3])"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -332,7 +332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 78,
    "id": "3a25ca11",
    "metadata": {},
    "outputs": [
@@ -344,7 +344,7 @@
        "        [7, 8, 9]])"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -363,19 +363,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 79,
    "id": "7526c1ee",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "tensor([[0.8956, 0.3751, 0.1271, 0.0786],\n",
-       "        [0.6605, 0.9228, 0.6594, 0.6637],\n",
-       "        [0.8143, 0.3942, 0.9663, 0.9637]])"
+       "tensor([[0.1109, 0.4425, 0.1133, 0.9341],\n",
+       "        [0.7576, 0.9063, 0.7243, 0.5338],\n",
+       "        [0.3031, 0.8889, 0.0658, 0.3763]])"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -388,7 +388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 80,
    "id": "26cc4026",
    "metadata": {},
    "outputs": [
@@ -398,7 +398,7 @@
        "2"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -409,7 +409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 81,
    "id": "bda29746",
    "metadata": {},
    "outputs": [
@@ -419,7 +419,7 @@
        "(torch.Size([3, 224, 224]), 3)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -440,7 +440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 82,
    "id": "9583ab83",
    "metadata": {},
    "outputs": [
@@ -452,7 +452,7 @@
        "        [0., 0., 0., 0.]])"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -464,7 +464,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 83,
    "id": "18cc934f",
    "metadata": {},
    "outputs": [
@@ -476,7 +476,7 @@
        "        [1., 1., 1., 1.]])"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -488,7 +488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 84,
    "id": "fc494597",
    "metadata": {},
    "outputs": [
@@ -498,7 +498,7 @@
        "torch.float32"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -517,7 +517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 85,
    "id": "01ff10a6",
    "metadata": {},
    "outputs": [
@@ -529,7 +529,7 @@
        "         9.0000,  9.5000, 10.0000, 10.5000])"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 85,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -542,7 +542,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 86,
    "id": "00fcd1b0",
    "metadata": {},
    "outputs": [
@@ -552,7 +552,7 @@
        "tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -573,7 +573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 87,
    "id": "f9fbb1f3",
    "metadata": {},
    "outputs": [
@@ -583,7 +583,7 @@
        "tensor([3., 6., 9.])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 87,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -599,7 +599,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 88,
    "id": "661203f0",
    "metadata": {},
    "outputs": [
@@ -609,7 +609,7 @@
        "torch.float32"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 88,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -620,7 +620,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 89,
    "id": "931e8a6b",
    "metadata": {},
    "outputs": [
@@ -630,7 +630,7 @@
        "tensor([3., 6., 9.], dtype=torch.float16)"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 89,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -642,7 +642,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 90,
    "id": "79935a92",
    "metadata": {},
    "outputs": [
@@ -652,7 +652,7 @@
        "torch.float32"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -663,19 +663,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 91,
    "id": "b80e8014",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "tensor([[0.0459, 0.5510, 0.7350, 0.0445],\n",
-       "        [0.9295, 0.0692, 0.2381, 0.5553],\n",
-       "        [0.2460, 0.5533, 0.6115, 0.1076]])"
+       "tensor([[0.6552, 0.8326, 0.2693, 0.4535],\n",
+       "        [0.4237, 0.0072, 0.9796, 0.4627],\n",
+       "        [0.7492, 0.4209, 0.5884, 0.7448]])"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 91,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -689,7 +689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 92,
    "id": "81a5af8e",
    "metadata": {},
    "outputs": [
@@ -697,9 +697,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "tensor([[0.0459, 0.5510, 0.7350, 0.0445],\n",
-      "        [0.9295, 0.0692, 0.2381, 0.5553],\n",
-      "        [0.2460, 0.5533, 0.6115, 0.1076]])\n",
+      "tensor([[0.6552, 0.8326, 0.2693, 0.4535],\n",
+      "        [0.4237, 0.0072, 0.9796, 0.4627],\n",
+      "        [0.7492, 0.4209, 0.5884, 0.7448]])\n",
       "Datatype of tensor: torch.float32\n",
       "Shape of tensor: torch.Size([3, 4])\n",
       "Device of tensor: cpu\n"
@@ -725,7 +725,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 93,
    "id": "05fadd99",
    "metadata": {},
    "outputs": [
@@ -735,7 +735,7 @@
        "tensor([101, 102, 103])"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 93,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -748,7 +748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 94,
    "id": "cced037e",
    "metadata": {},
    "outputs": [
@@ -758,7 +758,7 @@
        "tensor([202, 204, 206])"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -778,7 +778,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 95,
    "id": "dae43f89",
    "metadata": {},
    "outputs": [
@@ -795,7 +795,7 @@
        "tensor([1, 4, 9])"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -809,7 +809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 96,
    "id": "5335eb6a",
    "metadata": {},
    "outputs": [
@@ -819,7 +819,7 @@
        "tensor(14)"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -831,7 +831,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 97,
    "id": "96214e1c",
    "metadata": {},
    "outputs": [
@@ -841,7 +841,7 @@
        "14"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -853,7 +853,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 98,
    "id": "839afbd2",
    "metadata": {},
    "outputs": [
@@ -861,8 +861,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 0 ns, sys: 2.13 ms, total: 2.13 ms\n",
-      "Wall time: 1.14 ms\n"
+      "CPU times: user 1.87 ms, sys: 993 μs, total: 2.86 ms\n",
+      "Wall time: 1.7 ms\n"
      ]
     },
     {
@@ -871,7 +871,7 @@
        "tensor(14)"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -886,7 +886,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 99,
    "id": "1c1c93ce",
    "metadata": {},
    "outputs": [
@@ -894,8 +894,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 501 μs, sys: 0 ns, total: 501 μs\n",
-      "Wall time: 616 μs\n"
+      "CPU times: user 718 μs, sys: 0 ns, total: 718 μs\n",
+      "Wall time: 541 μs\n"
      ]
     },
     {
@@ -904,7 +904,7 @@
        "tensor(14)"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -924,7 +924,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 100,
    "id": "837697b0",
    "metadata": {},
    "outputs": [
@@ -932,8 +932,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 355 μs, sys: 0 ns, total: 355 μs\n",
-      "Wall time: 323 μs\n"
+      "CPU times: user 733 μs, sys: 0 ns, total: 733 μs\n",
+      "Wall time: 608 μs\n"
      ]
     },
     {
@@ -942,7 +942,7 @@
        "tensor(14)"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -955,19 +955,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 101,
    "id": "12f61493",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(tensor([[0.4556, 0.5742],\n",
-       "         [1.0881, 0.6826]]),\n",
+       "(tensor([[0.9213, 0.9373],\n",
+       "         [0.9613, 1.0427]]),\n",
        " torch.Size([2, 2]))"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -979,7 +979,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 102,
    "id": "bef98c6b",
    "metadata": {},
    "outputs": [
@@ -1024,7 +1024,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 103,
    "id": "4b0c4fe3",
    "metadata": {},
    "outputs": [
@@ -1034,7 +1034,7 @@
        "tensor([ 0., 10., 20., 30., 40., 50., 60., 70., 80., 90.])"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1046,7 +1046,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 104,
    "id": "013597a9",
    "metadata": {},
    "outputs": [
@@ -1056,7 +1056,7 @@
        "(tensor(0.), tensor(0.))"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1067,7 +1067,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 105,
    "id": "c071479e",
    "metadata": {},
    "outputs": [
@@ -1077,7 +1077,7 @@
        "(tensor(90.), tensor(90.))"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1088,7 +1088,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 106,
    "id": "9bb59339",
    "metadata": {},
    "outputs": [
@@ -1098,7 +1098,7 @@
        "(tensor(45.), tensor(45.))"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 106,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1109,7 +1109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 107,
    "id": "a4896145",
    "metadata": {},
    "outputs": [
@@ -1119,7 +1119,7 @@
        "(tensor(450.), tensor(450.))"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 107,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1138,7 +1138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 108,
    "id": "abe4c14d",
    "metadata": {},
    "outputs": [
@@ -1148,40 +1148,40 @@
        "tensor([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90])"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "x = torch.tensor([ 122, 10, 20, 30, 40, 50, 60, 70, 80, 90])\n",
+    "x = torch.tensor([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90])\n",
     "x"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 109,
    "id": "7ef986e5",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "tensor(0)"
+       "(tensor(0), tensor(9))"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 109,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "x.argmin()"
+    "x.argmin(), x.argmax()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 110,
    "id": "7ad63bda",
    "metadata": {},
    "outputs": [
@@ -1191,7 +1191,7 @@
        "tensor(0)"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1201,17 +1201,378 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "id": "6b49deec",
    "metadata": {},
+   "source": [
+    "## Reshaping, stacking, sqeezing and unsqeezing tensors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "f0a1909b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(tensor([1., 2., 3., 4., 5., 6., 7., 8., 9.]), torch.Size([9]))"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "x = torch.arange(1., 10.)\n",
+    "x, x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "ba064450",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(tensor([[1.],\n",
+       "         [2.],\n",
+       "         [3.],\n",
+       "         [4.],\n",
+       "         [5.],\n",
+       "         [6.],\n",
+       "         [7.],\n",
+       "         [8.],\n",
+       "         [9.]]),\n",
+       " torch.Size([9, 1]))"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Add an extra dimension\n",
+    "x_reshaped = x.reshape(9, 1)\n",
+    "x_reshaped, x_reshaped.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "ed78b429",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(tensor([[1., 2., 3., 4., 5., 6., 7., 8., 9.]]), torch.Size([1, 9]))"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Change the view\n",
+    "z = x.view(1, 9)\n",
+    "z, z.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "cf403167",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(tensor([[5., 2., 3., 4., 5., 6., 7., 8., 9.]]),\n",
+       " tensor([5., 2., 3., 4., 5., 6., 7., 8., 9.]))"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Changing z chages x (they share memory)\n",
+    "z[:, 0] = 5\n",
+    "z, x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "ca8cb352",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[5., 5., 5., 5.],\n",
+       "        [2., 2., 2., 2.],\n",
+       "        [3., 3., 3., 3.],\n",
+       "        [4., 4., 4., 4.],\n",
+       "        [5., 5., 5., 5.],\n",
+       "        [6., 6., 6., 6.],\n",
+       "        [7., 7., 7., 7.],\n",
+       "        [8., 8., 8., 8.],\n",
+       "        [9., 9., 9., 9.]])"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Stack tensors on top of each other\n",
+    "x_stack = torch.stack([x,x,x,x],dim=1)\n",
+    "x_stack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "cdb0b28c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([9, 1])\n",
+      "tensor([5., 2., 3., 4., 5., 6., 7., 8., 9.])\n",
+      "torch.Size([9])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Sqeeze\n",
+    "print(x_reshaped.shape)\n",
+    "print(x_reshaped.squeeze())\n",
+    "print(x_reshaped.squeeze().shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "5f036e71",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([9, 1])\n",
+      "tensor([[[5.]],\n",
+      "\n",
+      "        [[2.]],\n",
+      "\n",
+      "        [[3.]],\n",
+      "\n",
+      "        [[4.]],\n",
+      "\n",
+      "        [[5.]],\n",
+      "\n",
+      "        [[6.]],\n",
+      "\n",
+      "        [[7.]],\n",
+      "\n",
+      "        [[8.]],\n",
+      "\n",
+      "        [[9.]]])\n",
+      "torch.Size([9, 1, 1])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Sqeeze\n",
+    "print(x_reshaped.shape)\n",
+    "print(x_reshaped.unsqueeze(dim=2))\n",
+    "print(x_reshaped.unsqueeze(dim=2).shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57a63474",
+   "metadata": {},
+   "source": [
+    "## Inxdexing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "bb3e71e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(tensor([[[1, 2, 3],\n",
+       "          [4, 5, 6],\n",
+       "          [7, 8, 9]]]),\n",
+       " torch.Size([1, 3, 3]))"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = torch.arange(1, 10).reshape(1, 3, 3)\n",
+    "x, x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "8c42f28e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[[1, 2, 3],\n",
+      "         [4, 5, 6],\n",
+      "         [7, 8, 9]]])\n",
+      "tensor([[1, 2, 3],\n",
+      "        [4, 5, 6],\n",
+      "        [7, 8, 9]])\n",
+      "tensor([1, 2, 3])\n",
+      "tensor(1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(x)\n",
+    "print(x[0])\n",
+    "print(x[0][0])\n",
+    "print(x[0][0][0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "ab0376d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[1, 2, 3]])"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x[:, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "c7ecdc4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[2, 5, 8]])"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x[:, :, 1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc2c0da2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([5])"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x[:, 1, 1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "20b11409",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([1, 2, 3])"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x[0, 0, :]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f3ba24e",
+   "metadata": {},
+   "source": [
+    "## PyTorch tensors & NumPy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7bb6813",
+   "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "import torch\n",
+    "import numpy as np\n",
+    "array = np.arange(1.0, 8.0)\n",
+    "tensor = torch.tensor"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv (3.11.15.final.0)",
+   "display_name": ".venv (3.13.7)",
    "language": "python",
    "name": "python3"
   },
@@ -1225,7 +1586,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.15"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,