image modifiée

.ipynb_checkpoints/Melanoma_starting_kit-checkpoint.ipynb

@@ -65,7 +65,7 @@
     "We provide a dataset already rescaled to 400 ☓ 400 pixels images and into grayscale by averaging the RGB channels and rescaling the picture using scikit learn library. \n",
     "\n",
     "<img src=\"./img/outfile.jpg\" width=\"500\" />\n",
-    "<div style=\"text-align: center; font-style: italic\">Rescaled picture of a mole</div>"
+    "<div style=\"text-align: center; font-style: italic\">Rescaled picture of a mole</div><br />"
    ]
   },
   {
@@ -580,14 +580,15 @@
    "metadata": {},
    "source": [
     "<img src=\"./img/workflow.png\" />\n",
-    "<div style=\"text-align: center; font-style: italic\">"
+    "<div style=\"text-align: center; font-style: italic\">Example of workflow</div>\n",
+    "<br />"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This Workflow allows to recognize the melanomas of the skins with a classification. If the model is well entrained, wich means that the percentage of false-negative is low and the accurate is better than 90% then it does not need to be entrained very often. In the fact, it has to be retrained when a new category of melanoma is discover. Indeed if the model is not retrained in this case, the rate of false-positive will increase and a lot of people will believe they are safe whearas they have a melanoma."
+    "This Workflow allows to recognize the melanomas of the skins with a classification. If the model is well trained, wich means that the percentage of false-negative is low and the accurate is better than 90% then it does not need to be trained very often. In the fact, it has to be retrained when a new category of melanoma is discover. Indeed if the model is not retrained in this case, the rate of false-positive will increase and a lot of people will believe they are safe whearas they have a melanoma."
    ]
   },
   {

Melanoma_starting_kit.ipynb

@@ -65,7 +65,7 @@
     "We provide a dataset already rescaled to 400 ☓ 400 pixels images and into grayscale by averaging the RGB channels and rescaling the picture using scikit learn library. \n",
     "\n",
     "<img src=\"./img/outfile.jpg\" width=\"500\" />\n",
-    "<div style=\"text-align: center; font-style: italic\">Rescaled picture of a mole</div>"
+    "<div style=\"text-align: center; font-style: italic\">Rescaled picture of a mole</div><br />"
    ]
   },
   {
@@ -588,7 +588,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This Workflow allows to recognize the melanomas of the skins with a classification. If the model is well entrained, wich means that the percentage of false-negative is low and the accurate is better than 90% then it does not need to be entrained very often. In the fact, it has to be retrained when a new category of melanoma is discover. Indeed if the model is not retrained in this case, the rate of false-positive will increase and a lot of people will believe they are safe whearas they have a melanoma."
+    "This Workflow allows to recognize the melanomas of the skins with a classification. If the model is well trained, wich means that the percentage of false-negative is low and the accurate is better than 90% then it does not need to be trained very often. In the fact, it has to be retrained when a new category of melanoma is discover. Indeed if the model is not retrained in this case, the rate of false-positive will increase and a lot of people will believe they are safe whearas they have a melanoma."
    ]
   },
   {