DeepSEA, a deep-learning algorithm trained on large-scale chromatin-profiling data, predicts chromatin effects from sequence alone, has single-nucleotide sensitivity and can predict effects of ...

Forecasting the future average speed fluctuation is conducive to timely perceiving the degree of urban road congestion. Nevertheless, a new challenge has arisen in accurately predicting the spot speed ...

Contribute to eather0056/Deep-Learning-for-Sequential-Data development by creating an account on GitHub. ... In this section, we delve into the implementation of the GPT architecture (Generative ...

This paper proposes an approach that uses a sequential dataset generated from game logs to feed into deep neural networks to predict victories. Among the six different deep neural networks implemented ...

New deep-learning approach predicts protein structure from amino acid sequence. ScienceDaily . Retrieved June 2, 2025 from www.sciencedaily.com / releases / 2019 / 04 / 190417111432.htm ...

In recent years, the transformer model has become one of the main highlights of advances in deep learning and deep neural networks. It is mainly used for advanced applications in natural language ...

This is an application of Keras Sequential model on the Churn Modeling data set. Objective was to develop an end to end model to train the model+ also the steps to preprocess the test data to align it ...