7 docs tagged with "Neural Networks"

In this post, we will explore Autoencoders, a type of artificial neural network used for unsupervised learning that focuses on efficiently encoding input data and reconstructing it.

This post explores Convolutional Neural Networks (CNN), a specialized neural network architecture widely used for tasks involving image processing and computer vision.

In this post, we'll explore Generative Adversarial Networks (GAN), a powerful class of neural networks used for generating new data based on learned distributions.

This post delves into Long Short-Term Memory (LSTM), a type of recurrent neural network designed to overcome the vanishing gradient problem, enabling better learning of long-term dependencies in sequential data.

Build and visualize neural networks with support for feedforward, convolutional, and recurrent architectures. Explore how these models learn from data using backpropagation and gradient descent.

Recurrent Neural Networks (RNNs) are a type of neural network designed to recognize patterns in sequences of data, including time-series data, language processing, and other sequence-related tasks.

This post explores Recurrent Neural Networks (RNN), a class of neural networks designed to handle sequential data and time-series information, commonly used for tasks involving natural language processing, speech recognition, and more.