chore(examples): proofreading the notebooks (#687)

---------

Co-authored-by: dante <45801863+alexander-camuto@users.noreply.github.com>

examples/notebooks/data_attest.ipynb

@@ -271,7 +271,7 @@
                 "The graph input for on chain data sources is formatted completely differently compared to file based data sources.\n",
                 "\n",
                 "- For file data sources, the raw floating point values that eventually get quantized, converted into field elements and stored in `witness.json` to be consumed by the circuit are stored. The output data contains the expected floating point values returned as outputs from running your vanilla pytorch model on the given inputs.\n",
-                "- For on chain data sources, the input_data field contains all the data necessary to read and format the on chain data into something digestable by EZKL (aka field elemenets :-D). \n",
+                "- For on chain data sources, the input_data field contains all the data necessary to read and format the on chain data into something digestable by EZKL (aka field elements :-D). \n",
                 "Here is what the schema for an on-chain data source graph input file should look like:\n",
                 "    \n",
                 "```json\n",

examples/notebooks/kzg_vis.ipynb

@@ -7,7 +7,7 @@
             "source": [
                 "# kzg-ezkl\n",
                 "\n",
-                "Here's an example leveraging EZKL whereby the inputs to the model, and the model params themselves, are commited to using kzg-commitments inside a circuit.\n",
+                "Here's an example leveraging EZKL whereby the inputs to the model, and the model params themselves, are committed to using kzg-commitments inside a circuit.\n",
                 "\n",
                 "In this setup:\n",
                 "- the commitments are publicly known to the prover and verifier\n",
@@ -166,7 +166,7 @@
                 "Shoutouts: \n",
                 "\n",
                 "- [summa-solvency](https://github.com/summa-dev/summa-solvency) for their help with the poseidon hashing chip. \n",
-                "- [timeofey](https://github.com/timoftime) for providing inspiration in our developement of the el-gamal encryption circuit in Halo2. "
+                "- [timeofey](https://github.com/timoftime) for providing inspiration in our development of the el-gamal encryption circuit in Halo2. "
             ]
         },
         {

examples/notebooks/simple_demo_public_network_output.ipynb

@@ -8,7 +8,7 @@
             "source": [
                 "## EZKL Jupyter Notebook Demo \n",
                 "\n",
-                "Here we demonstrate how to use the EZKL package to run a publicly known / committted to network on some private data, producing a public output.\n"
+                "Here we demonstrate how to use the EZKL package to run a publicly known / committed to network on some private data, producing a public output.\n"
             ]
         },
         {

examples/notebooks/tictactoe_binary_classification.ipynb

@@ -154,7 +154,7 @@
             "source": [
                 "## Create a neural net to verify the execution of the tic tac toe model\n",
                 "\n",
-                "1. Given the data generated above classify whether the tic tac toe games are valid. This approach uses a binary classification as the tic tac toe state space is fairly small. For larger state spaces we will want to use anomaly detection based approachs"
+                "1. Given the data generated above classify whether the tic tac toe games are valid. This approach uses a binary classification as the tic tac toe state space is fairly small. For larger state spaces, we will want to use anomaly detection based approaches."
             ]
         },
         {

examples/notebooks/world_rotation.ipynb

@@ -49,7 +49,7 @@
     "import torch\n",
     "import math\n",
     "\n",
-    "# these are constatns for the rotation\n",
+    "# these are constants for the rotation\n",
     "phi = torch.tensor(5 * math.pi / 180)\n",
     "s = torch.sin(phi)\n",
     "c = torch.cos(phi)\n",