How Uber Uses LSTM

The article introduces Ludwig, an open-source deep learning toolbox developed by Uber that allows users to train and test deep learning models without writing code.

The article discusses the exciting role of financial planning for data scientists at Uber, emphasizing the challenges and opportunities in modeling user growth and marketing spend.

The article discusses the evolution of Recurrent Neural Networks (RNNs), specifically focusing on the advancements made through evolutionary and reinforcement learning techniques.

The article discusses how Uber leverages Natural Language Processing (NLP) and machine learning to enhance its map data quality by analyzing customer feedback.

The article discusses Uber's innovative hybrid Exponential Smoothing-Recurrent Neural Network (ES-RNN) model that won the M4 Forecasting Competition.

This article discusses the implementation of uncertainty estimation in neural networks for time series prediction at Uber, focusing on the use of Bayesian neural networks (BNNs) and the Monte Carlo...

The article discusses Uber's approach to extreme event forecasting using Recurrent Neural Networks (RNNs), specifically Long Short Term Memory (LSTM) architecture.