Aim: Recognizing the Handwritten Digits to find the best accuracy using Machine learning methods such as Connectionist Temporal Classification (CTC) and Convolutional Neural Network (CNN). Methods and Materials: Accuracy and loss are performed with the MNIST dataset from the Keras library. The two groups Connectionist Temporal classification (N=20) and Convolutional Neural Network algorithms (N=20). Results: A CNN is used for recognizing the innovative handwritten digits. The accuracy is analysed based on correctness of the exact digits of 92.67% where the CTC has the accuracy of 89.07%. The two algorithms CNN and CTC are statistically satisfied with the independent sample T-Test (=.001) value (p<0.05) with confidence level of 95%. Conclusion: Recognizing the handwritten digits significantly seems to be better in CNN than CTC.

Product reviews are valuable for upcoming buyers in helping them make decisions. To this end, different opinion mining techniques have been proposed, where judging a review sentence�s orientation (e.g. positive or negative) is one of their key challenges. Recently, deep learning has emerged as an effective means for solving sentiment classification problems. Deep learning is a class of machine learning algorithms that learn in supervised and unsupervised manners. A neural network intrinsically learns a useful representation automatically without human efforts. However, the success of deep learning highly relies on the large-scale training data. We propose a novel deep learning framework for product review sentiment classification which employs prevalently available ratings supervision signals. The framework consists of two steps: (1) learning a high-level representation (an embedding space) which captures the general sentiment distribution of sentences through rating information; (2) adding a category layer on top of the embedding layer and use labelled sentences for supervised fine-tuning. We explore two kinds of low-level network structure for modelling review sentences, namely, convolutional function extractors and long temporary memory. Convolutional layer is the core building block of a CNN and it consists of kernels. Applications are image and video recognition, natural language processing, image classification