Goto

Collaborating Authors

Artificial Intelligence and Machine Learning Fundamentals

#artificialintelligence

Artificial Intelligence and Machine Learning Fundamentals Machine learning and neural networks are fast becoming pillars on which you can build intelligent applications. The course will begin by introducing . Description Machine learning and neural networks are fast becoming pillars on which you can build intelligent applications. The course will begin by introducing you to Python and discussing using AI search algorithms. You will learn math-heavy topics, such as regression and classification, illustrated by Python examples.


Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

Neural Information Processing Systems

The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time. Very few previous studies have examined this crucial and challenging weather forecasting problem from the machine learning perspective. In this paper, we formulate precipitation nowcasting as a spatiotemporal sequence forecasting problem in which both the input and the prediction target are spatiotemporal sequences. By extending the fully connected LSTM (FC-LSTM) to have convolutional structures in both the input-to-state and state-to-state transitions, we propose the convolutional LSTM (ConvLSTM) and use it to build an end-to-end trainable model for the precipitation nowcasting problem. Experiments show that our ConvLSTM network captures spatiotemporal correlations better and consistently outperforms FC-LSTM and the state-of-the-art operational ROVER algorithm for precipitation nowcasting.



Demystifying the Future Of Self-Supervised Deep Learning – IAM Network

#artificialintelligence

Self-supervised Learning is the solution for the limitations of Deep learning like requirement of large amount of data for huge computation process.


Building My First Deep Learning Machine

#artificialintelligence

First of all, myth busted: the 1080 Ti can run minesweeper effortlessly. The machine did restart itself once for no obvious reasons after the proprietary GPU driver was installed. Back to the topic… Here is some R code for fitting a "wide and deep" classification model with Tensorflow and Tensorflow Estimators API. The model is fundamentally a direct combination of a linear model and a DNN model. The synthetic data has 1 million observations, 100 features (20 being useful) and is generated by my R package msaenet.