neural network - OpenGenus IQ: Learn Computer Science

Understand Neural Networks intuitively

Neural Networks act as a ‘black box’ that takes inputs and predicts an output and it learns complex non-linear mappings to produce far more accurate output classification results.

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Understanding Convolutional Neural Networks through Image Classification

In this article, we explored the ideas involved in Convolutional Neural Networks (CNN) through Image Classification

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Autoencoder

An autoencoder is a neural network that learns data representations in an unsupervised manner. Its structure consists of Encoder, which learn the compact representation of input data, and Decoder, which decompresses it to reconstruct the input data.

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Recurrent Neural Networks (RNN)

Recurrent Neural Network is one of the widely used algorithms of Deep Learning mainly due to is unique Design. It is the only algorithm that remembers the most recent Input and makes use of memory element. It is used by Apple Siri and Google Voice Search. RNN is used for sequential data.

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Terms used in Neural Networks

The common terms used in Neural Networks are Convolution, Max Pooling, Fully Connected Layer, Softmax Activation Function and Rectified Linear Units.

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Feed Forward Neural Networks

A feedforward neural network is an Artificial Neural Network in which connections between the nodes do not form a cycle. Learn about how it uses ReLU and other activation functions, perceptrons, early stopping, overfitting, and others. See the architecture of various Feed Forward Neural Networks

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Types of Neural Network optimizations

The types of neural network optimizations are weight pruning, structured pruning, convolution, fully-connected, structured group, structure ranking with activations like Lp norm, block pruning, model thinning, compression schedule, regularization, group lasso, group variance, quantization and others