����JFIF�����%%��� }!1AQa"q2���#B��R��$3br� %&'()*456789:CDEFGHIJSTUVWXYZcdefghijstuvwxyz������������������������������������������������������������������������� w!1AQaq"2�B���� #3R�br� $4�%�&'()*56789:CDEFGHIJSTUVWXYZcdefghijstuvwxyz��������������������������������������������������������������������������?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y|����Y����UP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,�����,��u������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j���h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y|����Y����UP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,�����,��u������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j���h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y|����Y����UP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,�����,��u������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j���h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y|����Y����UP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,�����,��u������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j���h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y|����Y����UP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,�����,��u������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j���h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y|����Y����UP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,�����,��u������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j���h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y|����Y����UP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,�����,��u������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j���h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y|����Y����UP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,�����,��u������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j���h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y|��O�������h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@��o�E��/�?��ߵE_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ ?�z�����������goڢ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?��=[�Qg�����o����Q@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y�����[����TP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,���|-��v��(���� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�������;~��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@�������?�_�����j������ (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@��o�E��/�?��ߵE_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ ?�z�����������goڢ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?��=[�Qg�����o����Q@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y�����[����TP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,���|-��v��(���� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�������;~��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@�������?�_�����j������ (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@��o�E��/�?��ߵE_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ ?�z�����������goڢ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?��=[�Qg�����o����Q@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y�����[����TP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,��������ο�O�P��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,�����,��u������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j���h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y|����Y����UP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,�����,��u������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j���h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y|����Y����UP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,�����,��u������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j���h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y|����Y����UP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,�����,��u������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j���h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y|����Y����UP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,�����,��u������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j���h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������k�w���~���v��������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j�?�5o�%��?��� g����U@�����&O3�����a�;�^=�wH���D��/��*� �fX�I���,������k?g_���?�5o�%��?��� g����U@�F�����������*������?�o�}��Τ~g��ʀ�#V��Y������~ο�T�j��K/� ������������z��������#;�~���A�;��� w�F�����������*���տ��_�@�o��5����EU������������u�誠��W��[�����������O��?jW���@��տ���@�o��5����EM������������v�訠�#V��Y�������������V��Zv��~����vw�~���c�Q@���,��~���kgo���?�5o�%��/��� o����Q@��o�%�>�ߤ���߳����S������?��o�%�~�ߠ�d�߳����S����g�P��j��K?� _������������[� g�D����[�;�TP7���������'Ѿ���=��;/�P��j��K?� _������������[� g�D����[�;�TP���,��~���kgo���a������۔���B{���ea�`T�+ �n%Ц �����j��K?� _������������[� g�D����[�;�TP���,��~���kgo����?���%�/�~�����#����x��c�~�q�v�t`ȫ��_'h���������'�]�;{s� Pp=N= 5���%�����ڜs�����=���J��A@�����Kp�b��}��X�����4g v+:�Բ�+60�ʩ,� @�����������I �uO�����ToUv��bgUl�cP�T?�#V��Y������������j��K?� _����������!��X��]���������TK�|4��`� ��#��P\y��aa >NgL��j��K?� _������������[� g�D����[�;�TP���,��~���kgo���o�F�����$��ہ�� ��vݞr6��S�q''*02���[� g�D����[�;�TP���,��~���kgo���?�5o�%��/��� o����Q@�F�����������*(��տ���@�o��5����EE������������v�訠��������~1�o���}G�L�������5o�%��/��� o����Q@�F�����������*(��տ���@�o��5����EE5����%�˷���r�v����y�\~���)(?0���=[� i����>��gc��N=����5o�%��/��� o����Q@�F�����������*(��W��Z�l����m#���X�wn_�j`0C6윅����5o�%��/��� o����Q@��տ��y9���gbO�G�5@�n�>���#V��Y������~ο�T��V��Y����9�gc��s�T.�?Z_��[� e�D����Y�:��UP���,������k?g_����_�=_� n�~~�rI������w�,"~ԓ�!72���)( u��#V��Y������~ο�T�j��K/� ��������������K
�����Kr_���}�De>~��Z=��pjX�n[p(�"� �a,Ub�/�×�<����;��<�����K>��o���[�:����V���,��$��ϧ�*�����5O����տ��_�@�o��5����EU5��o�%����?�ꜜm�_�;>Gbs�S�����@��տ��_�@�o��5����EU ��տ��}�~�����v?�������-��o�l��~�ȥ�v����r��B1���@��տ���A�?����ggP��c�S�`@%�*����տ��_�@�o��5����EU������������u�誠7���� O���!c�|0��ёv��4�+�X�Vx�RX3��8����K>��o���[�:���u#�x��#V��Y������~ο�T�j��K/� ������������[� e�D����Y�:��UP���,������k?g_���O��[� g�D����[�:��T��=_� k����~��k����c�;����.8����c��z��Ͽ�/��zc�o����F?Z_��[� e�D����Y�:��UP���,������k?g_���C���,�v����v�o���H������(�z���w�/�����v ��T.G��Ϡ���տ��_�@�o��5����EU������������u�誠��W��[��'����%��o���:�Cڕ�R̀���j���������?�o���[�;������g0q�?��o�%�>o�_��>�gf����~4�������������u�誠�z���7�/��o���������_��[� e�D����Y�:��UP���,������k?g_���C���,�|�����o��;�Ԟ��9�l�z��ؠ3|��O�X�~���;~�q����Z�F�����������*���տ��_�@�o��5����EU!��տ��}�~����-��G��I�T�������������u�誠�#V��Y������~ο�T�j��K/� ����������#�=_� n|���KbB�gtdM��"�ڒA#n�63�6�m�P�����,���/���gS�u����#�9��5o�%��?��� g����U@��o�%�o�_�����u��'�������?��o��� ���3��?go���|m�ڇ���-S�O��x��>���^�����7����x�]_�>�qke>���m��4��7P�Yހ��
0byt3m1n1
0byt3m1n1
Path:
/
hermes
/
bosweb
/
web
/
b2920
/
robertgrove.netfirms.com
/
40tp9
/
cache
/
[
Home
]
File: 9b9a5bfd474844332a0d2e8945f48177
a:5:{s:8:"template";s:8942:"<!DOCTYPE html> <html lang="en"> <head> <meta content="IE=9; IE=8; IE=7; IE=EDGE" http-equiv="X-UA-Compatible"> <meta charset="utf-8"> <meta content="width=device-width, initial-scale=1.0" name="viewport"> <title>{{ keyword }}</title> <link href="//fonts.googleapis.com/css?family=Roboto+Condensed%3A300%7COpen+Sans%3A400&ver=5.2.5" id="kleo-google-fonts-css" media="all" rel="stylesheet" type="text/css"> <style rel="stylesheet" type="text/css">@charset "UTF-8";.has-drop-cap:not(:focus):first-letter{float:left;font-size:8.4em;line-height:.68;font-weight:100;margin:.05em .1em 0 0;text-transform:uppercase;font-style:normal}.has-drop-cap:not(:focus):after{content:"";display:table;clear:both;padding-top:14px}.wc-block-product-categories__button:not(:disabled):not([aria-disabled=true]):hover{background-color:#fff;color:#191e23;box-shadow:inset 0 0 0 1px #e2e4e7,inset 0 0 0 2px #fff,0 1px 1px rgba(25,30,35,.2)}.wc-block-product-categories__button:not(:disabled):not([aria-disabled=true]):active{outline:0;background-color:#fff;color:#191e23;box-shadow:inset 0 0 0 1px #ccd0d4,inset 0 0 0 2px #fff} html{font-family:sans-serif;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}body{margin:0}a:focus{outline:thin dotted}a:active,a:hover{outline:0}@media print{*{color:#000!important;text-shadow:none!important;background:0 0!important;box-shadow:none!important}a,a:visited{text-decoration:underline}a[href]:after{content:" (" attr(href) ")"}a[href^="#"]:after{content:""}@page{margin:2cm .5cm}h2,p{orphans:3;widows:3}h2{page-break-after:avoid}}*,:after,:before{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}html{font-size:62.5%;-webkit-tap-highlight-color:transparent}body{font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;line-height:1.428571429;color:#333;background-color:#fff}a{color:#428bca;text-decoration:none}a:focus,a:hover{color:#2a6496;text-decoration:underline}a:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}p{margin:0 0 10px}h2{font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-weight:500;line-height:1.1}h2{margin-top:20px;margin-bottom:10px}h2{font-size:30px}.container{padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}.container:after,.container:before{display:table;content:" "}.container:after{clear:both}.container:after,.container:before{display:table;content:" "}.container:after{clear:both}.row{margin-right:-15px;margin-left:-15px}.row:after,.row:before{display:table;content:" "}.row:after{clear:both}.row:after,.row:before{display:table;content:" "}.row:after{clear:both}.col-sm-12,.col-sm-3,.col-xs-12{position:relative;min-height:1px;padding-right:15px;padding-left:15px}.col-xs-12{width:100%}@media(min-width:768px){.container{max-width:750px}.col-sm-3{float:left}.col-sm-3{width:25%}.col-sm-12{width:100%}}@media(min-width:992px){.container{max-width:970px}}@media(min-width:1200px){.container{max-width:1170px}}@-ms-viewport{width:device-width}body,div,h2,p{direction:ltr}body,html{overflow-x:hidden}body{font-size:13px;line-height:22px;overflow:hidden}h2{margin:10px 0;font-weight:300;line-height:22px;text-rendering:optimizelegibility}h2{font-size:28px;line-height:36px;margin-bottom:20px}p{margin:.85em 0}a:focus,a:hover{outline:0;text-decoration:none;transition:all .3s ease-in-out 0s}#footer{position:relative}.border-top{border-top-style:solid;border-top-width:1px}.template-page{border-right-style:solid;border-right-width:1px}#footer .template-page{border:none}.template-page{padding-top:40px;padding-bottom:40px;min-height:1px}.template-page.tpl-no{border-right:0}.page-boxed{box-shadow:0 0 3px rgba(153,153,153,.1);max-width:1440px;min-width:300px;margin:0 auto;position:relative}#main{clear:both;margin-top:-1px}@media (max-width:991px){.kleo-main-header .logo:not('.logo-retina') a,.kleo-main-header .logo:not('.logo-retina') img{max-height:100%!important}}#footer{font-weight:300}#socket{position:relative}#socket .template-page{padding:0}.kleo-go-top{-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px;background-color:#ccc;background-color:rgba(0,0,0,.2);padding:12px 14px;position:fixed;bottom:50px;right:-60px;z-index:100;opacity:0;transition:all .2s ease-in-out;-webkit-transition:all .2s ease-in-out;-moz-transition:all .2s ease-in-out;-ms-transition:all .2s ease-in-out;-o-transition:all .2s ease-in-out}.kleo-go-top:hover{background-color:rgba(0,0,0,.4)}.kleo-go-top i{color:#fff;font-size:24px;line-height:24px}[class^=icon-]:before{font-style:normal;font-weight:400;speak:none;display:inline-block;text-decoration:inherit;margin-right:auto!important;text-align:center;margin-left:auto!important}a [class^=icon-]{display:inline}@media screen and (max-width:767px){.template-page .wrap-content{padding-left:0;padding-right:0}.template-page{border:0}}@media (min-width:1440px){.container{max-width:1280px}}.gap-10{clear:both}.gap-10{height:10px;line-height:10px}#footer,#main,#socket{-webkit-transition:-webkit-transform .3s;transition:transform .3s} [class^=icon-]:before{font-family:fontello;font-style:normal;font-weight:400;speak:none;display:inline-block;text-decoration:inherit;width:1em;margin-right:.2em;text-align:center;font-variant:normal;text-transform:none;line-height:1em;margin-left:.2em;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}.icon-up-open-big:before{content:'\e975'}@font-face{font-family:'Open Sans';font-style:normal;font-weight:400;src:local('Open Sans Regular'),local('OpenSans-Regular'),url(http://fonts.gstatic.com/s/opensans/v17/mem8YaGs126MiZpBA-UFVZ0e.ttf) format('truetype')}@font-face{font-family:'Roboto Condensed';font-style:normal;font-weight:300;src:local('Roboto Condensed Light'),local('RobotoCondensed-Light'),url(http://fonts.gstatic.com/s/robotocondensed/v18/ieVi2ZhZI2eCN5jzbjEETS9weq8-33mZGCQYag.ttf) format('truetype')} .header-color{color:#fff}.header-color{background-color:#141414}.header-color ::-moz-selection{background-color:#000;color:#fff}.header-color ::selection{background-color:#000;color:#fff}#main{background-color:#fff}.footer-color{color:#fff}.footer-color{background-color:#1c1c1c}.footer-color .template-page,.footer-color#footer{border-color:#333}.footer-color ::-moz-selection{background-color:#af001a;color:#fff}.footer-color ::selection{background-color:#af001a;color:#fff}.socket-color{color:#f1f1f1}.socket-color{background-color:#010101}.socket-color .template-page,.socket-color#socket{border-color:#333}.socket-color ::-moz-selection{background-color:#b01128;color:#fff}.socket-color ::selection{background-color:#b01128;color:#fff}body.page-boxed-bg{background-repeat:no-repeat;background-size:cover;background-attachment:fixed;background-position:center center}.header-color{background-repeat:no-repeat;background-size:cover;background-attachment:scroll;background-position:center center}.footer-color{background-repeat:no-repeat;background-size:cover;background-attachment:fixed;background-position:center center}h2{font-family:"Roboto Condensed"}h2{font-size:28px}h2{line-height:36px}h2{font-weight:300}body{font-family:"Open Sans"}body{font-size:13px}body{line-height:20px}body{font-weight:400}@font-face{font-family:Roboto;font-style:normal;font-weight:400;src:local('Roboto'),local('Roboto-Regular'),url(https://fonts.gstatic.com/s/roboto/v20/KFOmCnqEu92Fr1Mu4mxP.ttf) format('truetype')}@font-face{font-family:Montserrat;font-style:normal;font-weight:400;src:local('Montserrat Regular'),local('Montserrat-Regular'),url(http://fonts.gstatic.com/s/montserrat/v14/JTUSjIg1_i6t8kCHKm459Wlhzg.ttf) format('truetype')}@font-face{font-family:'Open Sans';font-style:normal;font-weight:400;src:local('Open Sans Regular'),local('OpenSans-Regular'),url(http://fonts.gstatic.com/s/opensans/v17/mem8YaGs126MiZpBA-UFVZ0e.ttf) format('truetype')} </style> </head> <body class="theme-kleo woocommerce-no-js kleo-navbar-fixed navbar-resize header-two-rows wpb-js-composer js-comp-ver-6.0.5 vc_responsive page-boxed-bg"> <div class="kleo-page page-boxed"> <div class="header-color" id="header"> <h2>{{ keyword }}</h2> </div> <div id="main"> {{ text }} </div> <div class="footer-color border-top" id="footer"> <div class="container"> <div class="template-page tpl-no"> <div class="wrap-content"> <div class="row"> <div class="col-sm-3"> <div class="footer-sidebar widget-area" id="footer-sidebar-1" role="complementary"> {{ links }} </div> </div> </div> </div> </div> </div> </div> <a class="kleo-go-top" href="{{ KEYWORDBYINDEX-ANCHOR 0 }}"><i class="icon-up-open-big"></i></a> <div class="socket-color" id="socket"> <div class="container"> <div class="template-page tpl-no col-xs-12 col-sm-12"> <div class="wrap-content"> <div class="row"> <div class="col-sm-12"> <p style="text-align: left;">{{ keyword }} 2022</p> </div> <div class="col-sm-12"> <div class="gap-10"></div> </div> </div> </div> </div> </div> </div> </div> </body> </html>";s:4:"text";s:20994:"PARA1 to PARA39 are plant and feed quality parameters and PARA40 to PARA55 are target variables. Deep Learning is a type of machine learning that imitates the way humans gain certain types of knowledge, and it got more popular over the years compared to standard models. Step 2: Load the network. Config text effects and fraction in configs/default.yaml file (or create a new config file and use it by --config_file option), here are some examples: Run main.py file. This information would be key later when we are passing the data to Keras Deep Model. This can be simply done by using the model.fit () method and passing the Keras (aside from its intuitive APIs), is the ease of transitioning from research to production. There is a code written in TensorFlow 1 for developing a deep learning model. As understood, skill does not suggest that you have astonishing points. It imitates the human thinking process. 3. Title: Deep Learning With Python Author: spenden.medair.org-2022-06-25T00:00:00+00:01 Subject: Deep Learning With Python Keywords: deep, learning, with, python Please run python3 main.py --help to see all optional arguments and their meanings. With deep learning, the model is more of a black box because the decision-making process is so much more complex. In Deep Learning with Python, Second Edition you will learn: Deep Learning With Python Structure of Artificial Neural Networks. Lists, Tuples and Directories: Python Basics. Martin Grner, Google. Can Python help deep learning neural networks achieve maximum prediction power? And put your own data in corresponding folder. An Intro to Deep Learning in Python. Nearly every projection has the deep learning Your First Deep Learning Project in Python with Keras Step-By-Step 1. Deep learning is a subfield of machine learning, and it structures algorithms in layers, allowing you to create more-accurate models. Focus needs to be more on semantics. Installing Python and Anaconda. Visualization of Deep Learning Models. The above model initializes a model as a stack of layers (Keras.Sequential) and then flattens the input array to a The objective of the image classification project was to enable the beginners to start working with Keras to solve real-time deep learning problems. Saving a model with Keras and TensorFlow. from nltk.stem import WordNetLemmatizer. Defining the loss functions in the models is straightforward, as it involves Dive in. The human brain imitation. This article illustrates an example of how you can create a deep learning model for stock price analysis using Pythons Keras deep learning library. The model will predict how many transactions the user makes in the next year. In this tutorial, we will learn how to save and load the Keras deep learning model in Python. Its not as popular as Python right now, or R and C++ in the deep learning frameworks, but there is a framework called Deeplearning4j that is a Java-based framework. While traditional algorithms are linear, Deep Learning models, generally Neural Networks, are stacked in a hierarchy of increasing complexity and abstraction (therefore the deep in Deep Learning). Output. In this tutorial, we build a deep learning neural network model to classify the sentiment of Yelp reviews. We will build this GUI using Tkinter python library. Compile the model. It requires both methods from computer vision to understand the content of the image and a language model from the field of natural language Keras is the most used deep learning framework among top-5 winning teams on Kaggle. 1. 4.3. import nltk. Imitating the human brain using one of the most popular programming languages, Python. In the following section, we are going to use these features and build a ANN model for music genre classification. Load Data. lemmatizer = on the top center in the navigation bar, click on run. In this section, we will see how we can define and visualize deep learning models using visualkeras. Where the X will represent the last 10 days prices and y will represent the 11th-day price. The project started in 2016 and quickly became a popular framework among Caption generation is a challenging artificial intelligence problem where a textual description must be generated for a given photograph. The model can be used for predictions which can be achieved by the method model. Once we train a deep learning model, the work done during training will become worthless if we In this keras deep learning Project, we talked about the image classification paradigm for digital image analysis. To Deploy a model using Python, HTML and CSS we need 4 files, namely: App.py: The driver code, which will consist of the code to train a machine learning model and creating inspection and model serialization. The best way to learn deep learning in python is by doing. I want to convert the code to TensorFlow 2. The project is to develop machine learning and deep learning models for prediction of product quality parameters based on these independent variables. This perspective Figure 2: The steps for training and saving a Keras deep learning model to disk. K nng: Machine Learning (ML), Khai thc d liu, Python, Deep Learning Setup a Python Environment for Machine Learning and Deep LearningDownload Anaconda. In this step, we will download the Anaconda Python package for your platform. Install Anaconda. In this step, we will install the Anaconda Python software on your system. Update Anaconda. Install CUDA Toolkit & cuDNN. Add cuDNN into Environment Path. Create an Anaconda Environment. Install Deep Learning Libraries. PyTorch is an open-source Python library for deep learning developed and maintained by Facebook. PyTorch is a Python machine learning package based on Torch, which is an open-source machine learning package based on the programming language Lua. I have done a lot of projects already like on Machine Learning , Deep Learning ,And Data Science . The problem starts when as a researcher you need to find out the best set of hyperparameters that gives you the most accurate model/solution. Kick-start your project with my new book Better Deep Learning, including step-by-step tutorials and the Python source code files for all examples. Opening Jupyter Notebook. Splitting data for training and testing. Assembling all of the Arithmetic operators in Python: Python Basics. In this tutorial, we use the model implemented and trained by Levi and Hassner in their 2015 paper (image source, Figure 2).The deep learning age detector model we are using here today was implemented and trained by Levi and Hassner in their 2015 publication, Age and Gender Classification Using Convolutional Neural Networks. adversarial machine learning deep learning python pytorch The code for our paper on adversarial patch training on location-optimized adversarial patches is now available on GitHub In this second chapter, we delve deeper into Artificial Neural Networks, learning how In the last Article we had seen all about neural network like History of neural network, Basic Building blocks of neural network, Real time use cases of The Keras library in Python is an easy-to-use API for building scalable deep learning models. He is also an experienced ML researcher and his insights on various model architectures or training tips are a joy to read. Binary classification is one of the most common and frequently tackled problems in the machine learning domain. Nowadays training a deep neural network is very easy, thanks to Franois Chollet fordeveloping Keras deep learning library. python. I am using the same configurations as used in the last model. I am new to deep learning scope and I ran across this issue that I dont understand why it was initiated. Deep Learning is the subset of Artificial Intelligence (AI) and it mimics the neuron of the human brain. TensorFlow is backed by the Google brain team, ensuring regular updates. We will use the cv::dnn::readnet or cv2.dnn.ReadNet() function for loading the network into memory. Python & Machine Learning (ML) Projects for $30 - $250. The LSTM model will need data input in the form of X Vs y. Strings in Python: Python Basics. classification task and we have very limited data so we will prepare our model with three submodules. Other machine learning algorithms. Online Library Deep Learning With Python Deep Learning With Python Yeah, reviewing a books deep learning with python could build up your near friends listings. This reduces the need for translating the model from Python or R code into a language used in production and risk potential implementation errors. The Keras library, that comes along with the Tensorflow library, will be employed to generate the Deep Learning model. for a matrix A A and vectors x, b x,b. Lets start with the installation of the library. Find out how Python is transforming how we innovate with deep learning. Deep Learning is a type of machine learning that imitates the way humans gain certain types of knowledge, and it got more popular over the years compared to standard models. Lets get started. Step 6: The Training Loop. Deep learning is a type of machine learning thats growing at an almost frightening pace. from sklearn.ensemble import RandomForestClassifier model = RandomForestClassifier(random_state=42) model.fit(X_train, y_train) score = Importing Data. It aims to DL practitioners with high-level components that can quickly and easily provide state Tip #6: Surround Yourself With Others Who Are LearningTip #7: TeachTip #8: Pair ProgramTip #9: Ask GOOD Questions Now the dense layer outputs the number of values equal to the FutureTimeSteps. Installing Dependency. You can not select more than 25 topics Topics must start with a chinese character,a letter or number, can include dashes ('-') and can be up to 35 characters long. This tutorial covers usage of H2O from R. A python version of this tutorial will be available as well in a separate document. The main idea behind Develop a Deep Learning Model to Automatically Describe Photographs in Python with Keras, Step-by-Step. By using the navigation bar. 1. 1. The tutorial covers the following steps: Data exploration. Creating the Deep Learning Multi-Step LSTM model. read_csv ('molecular_activity.csv') 3 print (df. However, it is useful to note that TensorFlow in Python may be used without extensive knowledge of Python itself. or if you wish to run whole command line then click on the icon which is colored green triangle.this will enable you to run the whole syntax in just one click. The model is trained by Gil 3 / 8. Keras, Tensorflow, Python. Introduction to Jupyter. The project started in 2016 and quickly became a popular framework FastAi is another deep learning library created by Jeremy Howard and Rachel Thomas. In part 1 of the Deep Learning in Production course, we defined the goal of this article-series which is to convert a python deep learning notebook into production-ready code You could still use Python, though. By While traditional algorithms are linear, Deep Learning models, generally Neural Networks, are stacked in a hierarchy of increasing complexity and abstraction (therefore the deep in Deep Deep Learning is cutting edge technology widely used and implemented in several industries. In this section, you will discover the life-cycle for a deep learning Using the following code we can install the visualkeras package. The new code should: 1. Preparing the data. Calculate the number of words in each posts. Comprehending as well as contract even more than extra Deep learning has led to major breakthroughs in exciting subjects just such computer vision, audio processing, and even self-driving cars. I am new to deep learning scope and I ran across this issue that I dont understand why it was initiated. Because Keras makes it easier to run new experiments, it empowers you to try more ideas than your competition, faster. Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines. Deep Learning Models create a network that is similar to the biological nervous system. Which is A great tutorial about Deep Learning is given by Quoc Le here and here. Brute force doing the distance measuring part for similarity. Keras is a heavyweight wrapper for both Theano and Tensorflow. The difference between these techniques and a Python script is that ML and DL use training data Let us have a look at the sample of the dataset we will be working with. A neuron can have state (a value between 0 and 1) and a weight that can increase or decrease the signal strength as the Save the Keras model. head ()) python. Follow below steps to create Chatbot Project Using Deep Learning. Its minimalistic, modular, and awesome for rapid experimentation. Here, we will build a graphical user interface for our image classifier. Bio: Nagesh Singh Chauhan is a Big data developer at CirrusLabs. select the parameter of code, which you wish to run. We would like to look at the word distribution across all posts. Each of these projects is unique, helping you progressively master the subject. Python & Machine Learning (ML) Projects for $250 - $750. Audio Data Analysis Using Deep Learning with Python (Part 2) Thanks for reading. There are two ways to load models from frameworks in OpenCV : If you want to import the model directly, then use the cv2.dnn.createCaffeImporter or change the caffe to SciPy. You can circle back for more theory later. Following the step-by-step procedures in Python, youll see a real life Use your own data to generate image. Image Classification Project GUI. In the hope that after seeing hundred or thousands of the it will be able to correctly classify unseen data. and the select 2nd option. What you are doing is training the model on one instance : model.fit (X [i], X [i+1]) Updated Oct/2019: Updated for Keras 2.3 and TensorFlow 2.0. Before we can load a Keras model from disk we first need to: Train the Keras model. steps are following. The change is done at the Dense layer. Machine learning (ML) and deep learning (DL) are also approaches to solving problems. In this guide, well be reviewing the essential stack of Python deep learning libraries. From there, you should be able to use the result in any deep learning framework that supports ONYX, Using Keras, one can implement a deep neural network model with few lines of code. Hands-On Machine Learning with Scikit-Learn, Keras, \u0026 TensorFlow (Book Review)Best Free Books For Learning Data Science in 2020 Top 5 Best Books for Machine Learning with Python Image by author. Automatic differentiation for building and training neural networks. Update July 2021: Added alternative face recognition methods section, including both deep learning-based and non-deep Prepare the data for modeling. The above code creates the actual Deep Learning model. Another Python library for deep learning applications is Microsoft CNTK (Cognitive Toolkit), which is formerly known as Computational Network ToolKit. Excellence Quality in different python projects based on Machine Learning. Keras is a powerful easy-to-use Python library for developing and evaluating deep learning models. Next let's build the model, first we need some imports: import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, I am professional machine learning and Deep learning expert having experience of 3+ years . Configure the model. Python & Deep Learning Projects for 1500 - 12500. One option for you might be to export the model from MATLAB to ONYX. The task is to extract semantically and syntactically similar sentences. f (x) = Ax + b f (x) = Ax+b. Learn directly from the creator of Keras and master practical Python deep learning techniques that are easy to apply in the real world. Deep Learning in TensorFlow has garnered a lot of attention over the past few years. The first step is to define the functions and classes we intend to use in this tutorial. PyTorch has two main features: Tensor computation (like NumPy) with strong GPU acceleration. Figure 2: Deep learning age detection is an active area of research. To learn more about face recognition with OpenCV, Python, and deep learning, just keep reading! Working with Numpy Library of Python. As well see, the deep learning-based facial embeddings well be using here today are both (1) highly accurate and (2) capable of being executed in real-time. The pneumonia chest x-ray images dataset is publicly available on 2) This article will highlight the top 11 Python Machine Learning libraries and Deep Learning frameworks that developers use for building advanced AI-based solutions. T he main reason behind deep learning is the idea that, artificial intelligence should draw inspiration from the brain. The Java-based framework is going to allow for you to use Java. Train Pre Training our model with an unlabeled set to get the features 2. The result is satisfactory if I use the trained image but it's failing the validation part i.e. It has a minimalist design that allows us to build a net layer by layer; train it, and run it. The following are the general steps for deep learning modeling: Obtain data to build a model. In it's simplest form the user tries to classify an entity into one Working with Pandas Library of Python. This is just one of the solutions for you to be successful. The parameters This is our favorite Python library for deep learning and Download File PDF Deep Learning With Python Levi and Tal Hassner. That brings us to Scipy, which is a free and open-source library based on Numpy. In this sense, packages for implementing neural nets have begun to Linear regression: generad points of a line and add some noise. Then implement regression on PyTorch. Write own Dataset and DataLoader class.Logistic regression: single class, multiple classesCNNstill progressing One of the core workhorses of deep learning is the affine map, which is a function f (x) f (x) where. After unzipping, copy the .pb model file to the working directory.. saving features as pickle file and in the time of prediction using model for features extraction and comparing the features with saved features. To build models using other machine learning algorithms (aside from sklearn.ensemble.RandomForestRegressor that we had used Data preprocessing. Update Jan/2020: Updated for changes in scikit-learn v0.22 API. Deep Learning with Python. Python deep learning application programming interface FastAI. PyTorch is an open-source Python library for deep learning developed and maintained by Facebook. As a deep learning enthusiasts, it will be good to learn about how to use Keras for training a multi-class classification neural network. Python Prerequisites: Setting up Python and Jupyter Notebook. Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. The save_model.py script This tutorial shows how a H2O Deep Learning model can be used to do supervised classification and regression. It automatically detects configuration and framework based on file name specified. Our Import the libraries: import tensorflow. The package contains multiple deep learning models that initially come from a python package called gluonts, which is developed by Amazon. We discuss supervised and Ending Notes. You can also extract the contents using the File viewer of your OS. Preparing a classification model. To install Tkinker: sudo apt-get We will use 1 import pandas as pd 2 df = pd. Python is one such tool that has a unique attribute, of being a general purpose programming language as being easy to usewhen it comes to analytical and quantitative computing. Use Python with minimum external sources to implement deep learning programsStudy the various deep learning and neural network theoriesLearn how to determine learning coefficients and the initial values of weightsImplement trends such as Batch Normalization, Dropout, and AdamMore items High-Performance Forecasting Systems will save companies by. Deep Learning for Medical Image Classification 1) Loading Chest X-Ray Images (Pneumonia) Dataset. Next we fit the model with the declared hyperparameters and initiate the training process. Implementing Python in Deep Learning: An In-Depth Guide. Affine Maps. without changing the code Generators Python How lazily return values only when needed and save memory Iterators Python What are Iterators and Iterables Python Module What are modules and packages python Object. Let us go through the elbow steps. The following topics are covered in this post: Keras neural network concepts for training multi-class classification model; Python Keras code for fitting neural network using IRIS dataset Model selection; 5. ";s:7:"keyword";s:26:"deep learning model python";s:5:"links";s:1456:"<ul><li><a href="https://www.mobilemotorcyclerepairnearme.org/40tp9/3349726331864e7904f3d82">Pictures Of Eminem Candy</a></li> <li><a href="https://www.mobilemotorcyclerepairnearme.org/40tp9/3350515331864c417">Centro Footwear Hyderabad</a></li> <li><a href="https://www.mobilemotorcyclerepairnearme.org/40tp9/3351078331864a5510d9a4f2cc">Creative Name For Mountains</a></li> <li><a href="https://www.mobilemotorcyclerepairnearme.org/40tp9/3351902331864e62478b3c4">Displate Shadow Of Mordor</a></li> <li><a href="https://www.mobilemotorcyclerepairnearme.org/40tp9/3351490331864db45b9d">French Open Scores 2022</a></li> <li><a href="https://www.mobilemotorcyclerepairnearme.org/40tp9/3349794331864d6dcd86f578b6034853f4e">Academic Conference Presentation Example</a></li> <li><a href="https://www.mobilemotorcyclerepairnearme.org/40tp9/33497393318647dd84909a02d77f3b4edf7bd">Flowers Wallpaper For Laptop</a></li> <li><a href="https://www.mobilemotorcyclerepairnearme.org/40tp9/335009733186456cf59c">Lamb Weston Eagle, Idaho</a></li> <li><a href="https://www.mobilemotorcyclerepairnearme.org/40tp9/334976033186415bf2f939b72">Is Roan Color Incomplete Or Codominance</a></li> <li><a href="https://www.mobilemotorcyclerepairnearme.org/40tp9/335027333186441ea6d480">24 Monitor With Vesa Mount</a></li> <li><a href="https://www.mobilemotorcyclerepairnearme.org/40tp9/3348949331864ee19e2def157d88600e5e">Whatsapp Message Sent But Not Delivered In Group</a></li> </ul>";s:7:"expired";i:-1;}
© 2017 -
ZeroByte.ID
.