Deep Learning with Neural Networks-Part 3

Part 3: Convolutional Neural Networks (CNN)

This article, mainly discussing the Convolutional Neural Network(CNN) which is one of the main part of Supervised Learning with neural networks.

(1) What are Convolutional Neural Networks?

  • Convolutional Neural Networks are designed to address image recognition systems and classification problems.

(2) What are the applications of Convolutional Neural Networks?

  • Convolutional Neural Networks have wide applications in image and video recognition, recommendation systems, and Natural Language Processing.

(3) How do Convolutional Neural Networks work?

  • There are four layered concepts to understand Convolutional neural networks.
  1. Convolution Layer-Different positions, features recognition

(4) Training the Convolutional Neural Networks

  • One of the great challenges of developing CNNs is adjusting the weights of the individual neurons to extract the right features from images. The process of adjusting these weights is called “training” the neural network.

(5) The limits or drawbacks of Convolutional Neural Networks

  • CNNs are now widely used to moderate content on social media networks.

This article is mainly about Convolutional Neural networks (CNN)which are commonly used in Supervised Learning with neural networks. The next article is mainly focusing on Recurrent Neural networks (RNN)

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