In this challenge, we will treat electroencephalogram data and try to predict if a fixation is used for control or if it is a spontaneous one.
In this challenge, we are going to predict the final price of each house. We are given 2 data sets: train and test. Each data set contain many features that we will explore, try to find eventual correlations beween them and select the most useful ones to predict the house price. Finally, we will test and compare a few models trained on this data in order to select the model with the best performance.
In this challenge, we are going to predict the final price of each house. We are given 2 data sets: train and test. Each data set contain many features that we will explore, try to find eventual correlations beween them and select the most useful ones to predict the house price. Finally, we will test and compare a few models trained on this data in order to select the model with the best performance.
In this post, we will build, train and optimize in TensorFlow one of the simplest Convolutional Neural Networks, LeNet-5, proposed by Yann LeCun, Leon Bottou, Yosuha Bengio and Patrick Haffner in 1998.
A car company’s commercial project would be a combination of detection and classification of road signs inside the car software. This project is highly recommended for autonomous cars and even to automate some car functions such as alerting drivers on a limit speed or other road signs.