Introduction
This blog provides a concise yet comprehensive overview of some of the most important neural network types, including their fundamental workings and practical applications. Whether you're a beginner or a seasoned professional, this guide will help you grasp the essential concepts and explore resources for deeper learning.
Perceptron
The simplest form of a neural network, consisting of a single neuron with adjustable weights and bias. Used for binary classification tasks.
Pallavi Bharadwaj: https://towardsdatascience.com/perceptron-and-its-implementation-in-python-f87d6c7aa428
Feedforward Neural Network (FNN)
Information flows in one direction, from input to output, without cycles. Applied in pattern recognition and simple classification tasks.
Adarsh Menon:https://towardsdatascience.com/creating-neural-networks-from-scratch-in-python-6f02b5dd911
Multilayer Perceptron (MLP)
A type of feedforward neural network with multiple hidden layers, enabling the modeling of complex relationships. Used in classification and regression tasks.
Convolutional Neural Network (CNN)
Specialized for processing grid-like data such as images; employs convolutional layers to detect features. Commonly used in image and video recognition.
Recurrent Neural Network (RNN)
Designed for sequential data; has connections that form directed cycles, allowing information persistence. Applied in language modeling and time-series analysis.
Javaid Nabi:https://towardsdatascience.com/recurrent-neural-networks-rnns-3f06d7653a85
Abhinav Mishra: https://medium.com/@abhinav.mishra123/building-a-recurrent-neural-network-rnn-with-pytorch-9358a696d7dc
Long Short-Term Memory (LSTM)
A type of RNN capable of learning long-term dependencies; addresses the vanishing gradient problem. Utilized in speech recognition and text generation.
Ahmad Anis: https://cnvrg.io/pytorch-lstm/
deeplearningwizard:https://www.deeplearningwizard.com/deep_learning/practical_pytorch/pytorch_lstm_neuralnetwork/#step-6-instantiate-optimizer-class
Generative Adversarial Network (GAN)
Consists of a generator and a discriminator network; the generator creates data, and the discriminator evaluates it. Used for generating realistic images and data augmentation.
Venkatesh Tata(Good explanation with example):
https://becominghuman.ai/understanding-and-building-generative-adversarial-networks-gans-8de7c1dc0e25
Autoencoder
Learns to compress input data into a latent space representation and reconstruct it back; used for dimensionality reduction and denoising.
Syed Hasan:https://medium.com/@syed_hasan/autoencoders-theory-pytorch-implementation-a2e72f6f7cb7
Arxiv Paper:https://arxiv.org/pdf/2003.05991
Transformer
Employs self-attention mechanisms to process sequential data without relying on recurrence; excels in natural language processing tasks.
Longxiang He:https://say-hello2y.github.io/2022-08-18/transformer
bavalpreetsinghh:https://medium.com/@bavalpreetsinghh/transformer-from-scratch-using-pytorch-28a5d1b2e033
Arxiv Paper:https://arxiv.org/pdf/1706.03762
Top comments (0)