I can’t get through a zoom call, a conference talk, or an afternoon scroll through LinkedIn without hearing about vectors. Do you feel like the term vector is everywhere this year? It is. Vector actually means several different things and it's confusing. Vector means AI data, GIS locations, digital graphics, and a type of query optimization, and more. The terms and uses are related, sure. They all stem from the same original concept. However their practical applications are quite different.

So “Vector” is my choice for this year’s name collision of the year.

You will learn in this post how to:

• decompose double-seasonal time series
• detrend time series
• model and forecast double-seasonal time series with trend
• use two types of simple regression trees
• set important hyperparameters related to regression tree

Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements.

It is a challenging problem given the large number of observations produced each second, the temporal nature of the observations, and the lack of a clear way to relate accelerometer data to known movements.

Classical approaches to the problem involve hand crafting features from the time series data based on fixed-sized windows and training machine learning models, such as ensembles of decision trees. The difficulty is that this feature engineering requires deep expertise in the field.

Recently, deep learning methods such as recurrent neural networks and one-dimensional convolutional neural networks, or CNNs, have been shown to provide state-of-the-art results on challenging activity recognition tasks with little or no data feature engineering.

In this tutorial, you will discover the ‘Activity Recognition Using Smartphones‘ dataset for time series classification and how to load and explore the dataset in order to make it ready for predictive modeling.

Deep Learning has had a huge impact on computer science, making it possible to explore new frontiers of research and to develop amazingly useful products that millions of people use every day. Our internal deep learning infrastructure DistBelief, developed in 2011, has allowed Googlers to build ever larger neural networks and scale training to thousands of cores in our datacenters. We’ve used it to demonstrate that concepts like “cat” can be learned from unlabeled YouTube images, to improve speech recognition in the Google app by 25%, and to build image search in Google Photos. DistBelief also trained the Inception model that won Imagenet’s Large Scale Visual Recognition Challenge in 2014, and drove our experiments in automated image captioning as well as DeepDream.