diff --git a/learn_the_basics.rst b/learn_the_basics.rst
index c6a2edba..67073f51 100644
--- a/learn_the_basics.rst
+++ b/learn_the_basics.rst
@@ -9,6 +9,11 @@ Learn the basics
 
         Transpiling Kornia functions to TensorFlow.
 
+    .. grid-item-card:: Transpiling Models from PyTorch to TensorFlow
+        :link: learn_the_basics/torch_to_tf_models.ipynb
+
+        Transpiling PyTorch models to TensorFlow.
+
     .. grid-item-card:: Trace Code
         :link: learn_the_basics/03_trace_code.ipynb
 
@@ -29,6 +34,7 @@ Learn the basics
     :maxdepth: -1
 
     learn_the_basics/torch_to_tf_functions.ipynb
+    learn_the_basics/torch_to_tf_models.ipynb
     learn_the_basics/03_trace_code.ipynb
     learn_the_basics/05_lazy_vs_eager.ipynb
     learn_the_basics/06_how_to_use_decorators.ipynb
diff --git a/learn_the_basics/example_models.py b/learn_the_basics/example_models.py
new file mode 100644
index 00000000..d6fdd783
--- /dev/null
+++ b/learn_the_basics/example_models.py
@@ -0,0 +1,16 @@
+import torch
+
+
+class SimpleModel(torch.nn.Module):
+    def __init__(self):
+        super(SimpleModel, self).__init__()
+        self.conv1 = torch.nn.Conv2d(1, 3, kernel_size=3)
+        self.relu = torch.nn.ReLU()
+        self.fc = torch.nn.Linear(3 * 26 * 26, 10)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.relu(x)
+        x = torch.flatten(x, 1)
+        x = self.fc(x)
+        return x
diff --git a/learn_the_basics/torch_to_tf_models.ipynb b/learn_the_basics/torch_to_tf_models.ipynb
new file mode 100644
index 00000000..246416f6
--- /dev/null
+++ b/learn_the_basics/torch_to_tf_models.ipynb
@@ -0,0 +1,160 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Transpiling Models from PyTorch to TensorFlow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can install the dependencies required for this notebook by running the cell below ⬇️, or check out the [Get Started](https://ivy.dev/docs/overview/get_started.html) section of the docs to find out more about installing ivy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install ivy\n",
+    "!pip install torch\n",
+    "!pip install tensorflow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we'll go through an example of how any model written in PyTorch can be converted, and used in, TensorFlow via `ivy.transpile`. First, lets import a simple torch model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from example_models import SimpleModel\n",
+    "\n",
+    "\"\"\"\n",
+    "This model is defined as follows:\n",
+    "\n",
+    "class SimpleModel(torch.nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(SimpleModel, self).__init__()\n",
+    "        self.conv1 = torch.nn.Conv2d(1, 3, kernel_size=3)\n",
+    "        self.relu = torch.nn.ReLU()\n",
+    "        self.fc = torch.nn.Linear(3 * 26 * 26, 10)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = self.conv1(x)\n",
+    "        x = self.relu(x)\n",
+    "        x = torch.flatten(x, 1)\n",
+    "        x = self.fc(x)\n",
+    "        return x\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we can convert the model to tensorflow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ivy\n",
+    "\n",
+    "TFSimpleModel = ivy.transpile(SimpleModel, source=\"torch\", target=\"tensorflow\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can use the model with TensorFlow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TensorShape([1, 10])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "\n",
+    "tf_model = TFSimpleModel()\n",
+    "tf_model(tf.random.normal((1, 1, 28, 28))).shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also take advantage of TensorFlow-specific features, such as `tf.function`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TensorShape([1, 10])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compiled_model = tf.function(tf_model)\n",
+    "compiled_model(tf.random.normal((1, 1, 28, 28))).shape"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}