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Anubhav Kumar

Anubhav Kumar

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Bhavesh Badani

Bhavesh Badani

02-May-2024

Neural networks are composed of interconnected nodes (neurons) that process input data and learn from it. They adjust their internal parameters during training to improve predictions. There is a function called Activation functions determine whether a node “fires” based on its input. Now, as you asked, let’s explore some popular neural network architectures:

Feedforward Neural Networks (FNNs):

  • This is the simplest type of neural network which generally Consists of an input layer, one or more hidden layers, and an output layer. It is Used for tasks like regression and classification.

Convolutional Neural Networks (CNNs):

  • Designed for image and video recognition. It is Utilized as layers to extract features from input images. Often used in computer vision tasks.

Long Short-Term Memory (LSTM) Networks:

  • A type of RNN with memory cells. These Addresses the vanishing gradient problem. Widely used for natural language processing (NLP).

Generative Adversarial Networks (GANs):

  • Consist of a generator and a discriminator. Generally Used for generating new data (e.g., images, music) by learning from existing data. MostPopular in creative applications.
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