fix typos

Author: undisputedcoder

00_tensorflow_fundamentals.ipynb

@@ -48,7 +48,7 @@
         "\n",
         "TensorFlow is vast. But the main premise is simple: turn data into numbers (tensors) and build machine learning algorithms to find patterns in them.\n",
         "\n",
-        "In this notebook we cover some of the most fundamental TensorFlow operations, more specificially:\n",
+        "In this notebook we cover some of the most fundamental TensorFlow operations, more specifically:\n",
         "* Introduction to tensors (creating tensors)\n",
         "* Getting information from tensors (tensor attributes)\n",
         "* Manipulating tensors (tensor operations)\n",
@@ -481,9 +481,9 @@
         "* **scalar**: a single number.\n",
         "* **vector**: a number with direction (e.g. wind speed with direction).\n",
         "* **matrix**: a 2-dimensional array of numbers.\n",
-        "* **tensor**: an n-dimensional arrary of numbers (where n can be any number, a 0-dimension tensor is a scalar, a 1-dimension tensor is a vector). \n",
+        "* **tensor**: an n-dimensional array of numbers (where n can be any number, a 0-dimension tensor is a scalar, a 1-dimension tensor is a vector). \n",
         "\n",
-        "To add to the confusion, the terms matrix and tensor are often used interchangably.\n",
+        "To add to the confusion, the terms matrix and tensor are often used interchangeably.\n",
         "\n",
         "Going forward since we're using TensorFlow, everything we refer to and use will be tensors.\n",
         "\n",
@@ -543,7 +543,7 @@
         "id": "A4WrQ8c2ux15"
       },
       "source": [
-        "Now let's try to change one of the elements of the changable tensor."
+        "Now let's try to change one of the elements of the changeable tensor."
       ]
     },
     {
@@ -673,7 +673,7 @@
       "source": [
         "### Creating random tensors\n",
         "\n",
-        "Random tensors are tensors of some abitrary size which contain random numbers.\n",
+        "Random tensors are tensors of some arbitrary size which contain random numbers.\n",
         "\n",
         "Why would you want to create random tensors? \n",
         "\n",
@@ -809,7 +809,7 @@
         "\n",
         "Wait, why would you want to do that?\n",
         "\n",
-        "Let's say you working with 15,000 images of cats and dogs and the first 10,000 images of were of cats and the next 5,000 were of dogs. This order could effect how a neural network learns (it may overfit by learning the order of the data), instead, it might be a good idea to move your data around."
+        "Let's say you working with 15,000 images of cats and dogs and the first 10,000 images were of cats and the next 5,000 were of dogs. This order could effect how a neural network learns (it may overfit by learning the order of the data), instead, it might be a good idea to move your data around."
       ]
     },
     {
@@ -1108,7 +1108,7 @@
       "source": [
         "## Getting information from tensors (shape, rank, size)\n",
         "\n",
-        "There will be times when you'll want to get different pieces of information from your tensors, in particuluar, you should know the following tensor vocabulary:\n",
+        "There will be times when you'll want to get different pieces of information from your tensors, in particular, you should know the following tensor vocabulary:\n",
         "* **Shape:** The length (number of elements) of each of the dimensions of a tensor.\n",
         "* **Rank:** The number of tensor dimensions. A scalar has rank 0, a vector has rank 1, a matrix is rank 2, a tensor has rank n.\n",
         "* **Axis** or **Dimension:** A particular dimension of a tensor.\n",
@@ -1450,7 +1450,7 @@
       "source": [
         "## Manipulating tensors (tensor operations)\n",
         "\n",
-        "Finding patterns in tensors (numberical representation of data) requires manipulating them.\n",
+        "Finding patterns in tensors (numerical representation of data) requires manipulating them.\n",
         "\n",
         "Again, when building models in TensorFlow, much of this pattern discovery is done for you."
       ]
@@ -1463,7 +1463,7 @@
       "source": [
         "### Basic operations\n",
         "\n",
-        "You can perform many of the basic mathematical operations directly on tensors using Pyhton operators such as, `+`, `-`, `*`."
+        "You can perform many of the basic mathematical operations directly on tensors using Python operators such as, `+`, `-`, `*`."
       ]
     },
     {
@@ -2067,7 +2067,7 @@
         "\n",
         "### The dot product\n",
         "\n",
-        "Multiplying matrices by eachother is also referred to as the dot product.\n",
+        "Multiplying matrices by each other is also referred to as the dot product.\n",
         "\n",
         "You can perform the `tf.matmul()` operation using [`tf.tensordot()`](https://www.tensorflow.org/api_docs/python/tf/tensordot). "
       ]
@@ -3750,4 +3750,4 @@
       ]
     }
   ]
-}
+}