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@article{149620,
        author = {Chaitanya R and Amitkumar Marab and Varsha Patil and Zeba mariam},
        title = {Deep learning with perceptron using tensor flow library},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {7},
        number = {1},
        pages = {123-126},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=149620},
        abstract = {In recent years, Deep Learning, Machine Learning, and Artificial Intelligence are highly focused concepts of data science.  Deep learning has achieved success in the field of Computer Vision, Speech and Audio Processing, and Natural Language Processing. It has the strong learning ability that can improve utilization of datasets for the feature extraction compared to traditional Machine Learning Algorithm. Perceptron is the essential building block for creating a deep Neural Network. The perceptron model is the more general computational model. It analyzes the unsupervised data, making it a valuable tool for data analytics. A key task of this paper is to develop and analyze learning algorithm. It begins with deep learning with perceptron and how to apply it using TensorFlow to solve various issues. The main part of this paper is to make perceptron learning algorithm well behaved with non-separable training datasets. This type of algorithm is suitable for Machine Learning, Deep Learning, Pattern Recognition, and Connectionist Expert System.},
        keywords = {Deep Learning, Machine Learning, Perceptron Learning algorithm, TensorFlow},
        month = {},
        }