Convolutional Neural Network

Convolutional Neural Network forms the base of all computer vision applications. Uses like self-driving cars, object recognition, face recognition, etc. Simple neural networks struggle with images because they are slow at training and processing and have a large number of parameters. To overcome these issues, we use Convolutional Neural Networks.

Convolutional Neural Networks, Deep Learning, Image Classification

1. "Literature Survey in Computational Neural Network," Slogix. [Google Scholar] [Crossref]

2. "What are Convolution Layers?," GeeksforGeeks. [Google Scholar] [Crossref]

3. "Convolutional Neural Network — Lesson 9: Activation Functions in CNNs," Medium. [Google Scholar] [Crossref]

4. "CNN | Introduction to Pooling Layer," GeeksforGeeks. [Google Scholar] [Crossref]

• What the Desert Fathers Teach Data Scientists: Ancient Ascetic Principles for Ethical Machine-Learning Practice
• Comparative Analysis of Some Machine Learning Algorithms for the Classification of Ransomware
• Comparative Performance Analysis of Some Priority Queue Variants in Dijkstra’s Algorithm
• Transfer Learning in Detecting E-Assessment Malpractice from a Proctored Video Recordings.
• Dual-Modal Detection of Parkinson’s Disease: A Clinical Framework and Deep Learning Approach Using NeuroParkNet