Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI.

As machine learning moves towards edge deployment, lifelong learning becomes crucial due to evolving data distributions and new tasks. Yet, applying traditional methods to learn from vast, complex IoT ...

Following is what you need for this book: This book is for Machine Learning engineers and data scientists who want to learn about Recurrent Neural Network models with practical use-cases. Exposure to ...

This repository contains a simple implementation of a recurrent neural network. It allows you to train a single-layer RNN with stochastic gradient descent and backpropagation through time (BPTT). This ...

This article applies an evolutionary algorithm to synthesize recurrent neurons optimized for specific datasets. This adds another layer to the AutoML framework, targeting the internal structure of ...

Deep learning neural networks, exemplified by models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Generative Adversarial Networks (GANs), have achieved ...