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Deep Learning Basics: Part2


Next, Prof. Lai will introduce Major Types of Neural Networks:

  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Generative Adversarial Networks (GAN)

Convolutional Neural Network (CNN) was used mainly for images. Convolution is a mathematical operation that has been widely used in many fields, such as digital signal processing, electrical engineering, and physics. The Recurrent Neural Network (RNN) solves this problem by adding a “loop” in the hidden layers. The loop can keep the previous states of sequential data, which “remember” the temporal information.

Generative Adversarial Networks (GAN) was proposed by Ian Goodfellow. GAN consists of two sub-networks: a generator and a discriminator. The generator tries to generate fake images based on random inputs, while the discriminator tries to classify if the generated images are fake or real. The generator learns to generate more realistic images, while the discriminator learns to identify more challenging fake images.

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