final corrections

LICENSE

-MIT License
+GPL License
 
-Copyright (c) 2021 pawel rosikiewicz
+Copyright (c) 2021 Pawel Rosikiewicz
 
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
 
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
 
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
\ No newline at end of file

README.md

@@ -8,8 +8,7 @@ Practical part for AMLD 2021 Workshop based on the original work of Pawel Rosiki
 ### Comparison of 5000 AI methods for cancer detection and classyfication on dermatoscopic images from Harvard HAM10000 dataset using FastClassAI workbench
 
 Author: Pawel Rosikiewicz, Founder, and Team Leader at SwissAI  
-License: MIT  
-  
+
 The goal is to quickly test and optimize large number of ml and deep learning models and dataset preprocessing procedures, that are integrated in one python enviroment with FastClassAI workebench.
   
 Main Goals are: