Update 05.ipynb

Co-Authored-By: Ranuga <[email protected]>

Author: Programmer-RD-AI

05.ipynb

@@ -1,9 +1,54 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1bd9ec5f-fbb8-4f28-b911-1f2b31927c67",
+   "metadata": {},
+   "source": [
+    "# What is Transfer learning?\n",
+    "\n",
+    "Transfer learning is a technique in machine learning in which knowledge learned from a task is re-used in order to boost performance on a related task. For example, for image classification, knowledge gained while learning to recognize cars could be applied when trying to recognize trucks. Wikipedia\n",
+    "\n",
+    "## Why use transfer learning?\n",
+    "1. Can leverage an existing neural network architecture proven to work on problems similar to our own\n",
+    "2. Can leverage a woring network architecture which has already learned patterns on similar data to our own (so great performence with low data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1129b80d-f36f-46b9-9355-e9e2968c681f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('1.9.1', '0.10.1')"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torchvision\n",
+    "torch.__version__,torchvision.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c029fad0-6880-49d5-b677-8d7ddbd79504",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "155d404c-dc1d-4837-9d27-87034bb7ab67",
+   "id": "11d88c75-4a1f-42be-aef9-119fb66c32b0",
    "metadata": {},
    "outputs": [],
    "source": []