Deep Learning Through Convolutional Neural Networks
Pooja Kalange1, Megha Mutalikdesai2
1Ms. Pooja Kalange, Computer Department, Institute of Industrial & Computer Management & Research, Pune, India.
2Ms. Megha Desai, Computer Department, Institute of Industrial & Computer Management & Research, Pune, India.
Manuscript received on 15 August 2019. | Revised Manuscript received on 25 August 2019. | Manuscript published on 30 September 2019. | PP: 1463-1465 | Volume-8 Issue-3 September 2019 | Retrieval Number: B3770078219/19©BEIESP | DOI: 10.35940/ijrte.B3770.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Deep learning which is associated with the basics of Machine Learning has become popular over the years because of its fast paced adaptability and ability to handle complex problems. Prior to this technology breakthrough traditional methods of machine learning were used in applications of Image processing and pattern recognition, and analytics .With the advent of CNNs it has become easy to combat complex learning problems using the property of specificity and accuracy in CNN architectures and methodologies. This paper gives an introductory insights in CNNs like the feed-forward propagation networks and Back propagation Networks. The paper explains steps followed by CNNs for classifying the input and generating a predefined output. It also explains evolution of multiple Image CNN architectures which find applications in multiple domains of Computer Science like Image Processing & Segmentation, Pattern Recognition & Predictive Analytics, Text Analytics to name a few . Index Terms:
Keywords: Deep Learning, Convolutional Neural Network (CNN),CNN Architectures
Scope of the Article: Deep Learning