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Deep learning is a subset of machine learning that employs artificial neural networks with multiple hidden layers to learn data representations and complex patterns. Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks, and convolutional neural networks have achieved state-of-the-art performance in various applications like computer vision, speech recognition, natural language processing, and machine translation.
In this article, we will learn about different types of neural networks. Although there are tens of different neural networks or architectures present, but to start as a beginner, we will only focus on the top five neural networks.
Types
Neural networks come in various types, each designed for specific tasks.
Feedforward Neural Network (FNN):
Description: These are the simplest types of neural networks, where information flows in one direction, from input to output layers without any loops or cycles. A multilayer perceptron (MLP) is a misnomer for a modern feedforward artificial neural network.
Use Cases: FNNs are used for basic tasks like image classification, regression problems, and simple pattern recognition.
Convolutional Neural Network (CNN):
Description: CNNs are designed for processing grid-like data, such as images and videos, by using convolutional layers to automatically learn features from input data. We will learn about convolutional layers in the future
Use Cases: CNNs are commonly used for image recognition, object detection, facial recognition, and image generation.
Recurrent Neural Network (RNN):
Description: RNNs have loops in their architecture, allowing them to maintain a hidden state and process sequences of data, making them suitable for tasks involving sequential data.
Use Cases: RNNs are applied in natural language processing (NLP), speech recognition, time series analysis, and sentiment analysis.
Long Short-Term Memory (LSTM):
Description: LSTMs are a specialized type of RNN that can capture long-term dependencies in sequences by using memory cells and gates.
Use Cases: LSTMs are used in tasks like machine translation, speech recognition, and handwriting recognition.
Autoencoder:
Description: Autoencoders are neural networks used for unsupervised learning. They consist of an encoder and a decoder, aiming to compress and then reconstruct the input data.
Use Cases: Autoencoders are used in image denoising, anomaly detection, and feature learning.
Generative Adversarial Network (GAN):
Description: GANs consist of a generator and a discriminator that compete against each other. The generator tries to create data that is indistinguishable from real data, while the discriminator aims to differentiate between real and fake data.
Use Cases: GANs are used for image generation and data augmentation.
These are some of the key types of neural networks, each tailored to different problem domains and data structures. The choice of a neural network depends on the specific task and the nature of the data you are working with.