An evnet driven model that uses financial time series data with New York Times information to form a LSTM recurrent neural network. There are 3 models. The first 2 models are based on price and volume ...

Abstract: Recurrent neural networks (RNNs), particularly long short-term memory (LSTM) networks, have emerged as standard tools for tackling a wide range of time series applications, such as natural ...

ABSTRACT: This paper explores the application of various time series prediction models to forecast graphical processing unit (GPU) utilization and power draw for machine learning applications using ...

Learn how to build a fully connected, feedforward deep neural network from scratch in Python! This tutorial covers the theory, forward propagation, backpropagation, and coding step by step for a hands ...

Abstract: The efficient deployment of Recurrent Neural Networks (RNNs), particularly long short-term memory (LSTM) architectures, on edge devices has become increasingly important due to their ability ...

We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write the code, but it's entirely from scratch in python. We will code Deep Neural ...

ABSTRACT: An algorithm is being developed to conduct a computational experiment to study the dynamics of random processes in an asymmetric Markov chain with eight discrete states and continuous time.