diff --git a/BHPD/01-DNN-Regression.ipynb b/BHPD/01-DNN-Regression.ipynb
index 68b1a22481ccee858fe34240b635b3289f177e39..95599165d894f5ca02801743f0e638b31bb94024 100644
--- a/BHPD/01-DNN-Regression.ipynb
+++ b/BHPD/01-DNN-Regression.ipynb
@@ -1,40 +1,13 @@
 {
  "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import sys\n",
-    "sys.path.append('..')\n",
-    "import fidle.header"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "<div style=\"text-align: left\">\n",
-    "<!--     <img src=\"../fidle/img/00-Fidle-header-01.svg\" style=\"width:800\" /> -->\n",
+    "    <img src=\"../fidle/img/00-Fidle-header-01.svg\" style=\"width:800px\" />\n",
     "</div>\n",
     "\n",
-    "![this is a test](../fidle/img/00-Fidle-header-01.png)\n",
     "\n",
     "Deep Neural Network (DNN) - BHPD dataset\n",
     "========================================\n",
@@ -57,24 +30,6 @@
     " - Evaluate the result\n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/fidle/header.py b/fidle/header.py
deleted file mode 100644
index 70acf00071914dfd587f8ec000e713e0fe0444f8..0000000000000000000000000000000000000000
--- a/fidle/header.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from IPython.display import Image, SVG
-display(Image(filename='../fidle/img/00-Fidle-header-01.png'))
\ No newline at end of file