The second most fatal disease in the world is cancer. Skin cancer is one of the most common types of cancer and has been increasing rapidly in recent years. The early diagnosis of this disease increases the chance of treatment dra- matically. In this study, deep learning models are developed for skin cancer sub- type detection including a standard convolutional neural network (CNN), VGG16, Resnet50, MobileNet, and Xception. The parameters of the standard CNN model are regularized using batch normalization, dropout, and L2-norm regularization. The hyper-parameters of this model are optimized using grid search, in which early stopping is used to optimize the number of epochs. For the rest of the mod- els, transfer learning strategies are employed with and without fine-tuning as well as re-training from scratch. Data augmentation is performed for increasing the number of samples in the training set further. The performances of the models are evaluated on a Kaggle dataset that is developed for binary classification of skin images as malignant or benign. The best prediction accuracy of 87.88% is achieved using ResNet50 as the convolutional neural network model, which is re-trained from scratch and with data augmentation applied.