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How to use Dataset in TensorFlow – Towards Data Science

#artificialintelligence

As you should know, feed-dict is the slowest possible way to pass information to TensorFlow and it must be avoided. The correct way to feed data into your models is to use an input pipeline to ensure that the GPU has never to wait for new stuff to come in. Fortunately, TensorFlow has a built-in API, called Dataset to make it easier to accomplish this task. In this tutorial, we are going to see how we can create an input pipeline using it and how to feed the data into the model efficiently. This article will explain the basic mechanics of the Dataset, covering the most common use cases.


Prediction and Analysis of Time Series Data using Tensorflow

#artificialintelligence

In this article, we focus on'Time Series Data' which is a part of Sequence models. In essence, this represents a type of data that changes over time such as the weather of a particular place, the trend of behaviour of a group of people, the rate of change of data, the movement of body in a 2D or 3D space or the closing price for a particular stock in the markets. Analysis of time series data can be done for anything that has a'time' factor involved in it. So what can machine learning help us achieve over time series data? It can also be used to predict missing values in the data. There are certain keywords that always come up when dealing with time series data. The time series has a specific direction in which the data is moving in.


Time Series Prediction with TensorFlow

#artificialintelligence

In this article, we focus on'Time Series Data' which is a part of Sequence models. In this article, we focus on'Time Series Data' which is a part of Sequence models. In essence, this represents a type of data that changes over time such as the weather of a particular place, the trend of behaviour of a group of people, the rate of change of data, the movement of body in a 2D or 3D space or the closing price for a particular stock in the markets. Analysis of time series data can be done for anything that has a'time' factor involved in it. So what can machine learning help us achieve over time series data? It can also be used to predict missing values in the data. There are certain keywords that always come up when dealing with time series data.


Time Series Forecasting with the Long Short-Term Memory Network in Python - Machine Learning Mastery

@machinelearnbot

The Long Short-Term Memory recurrent neural network has the promise of learning long sequences of observations. It seems a perfect match for time series forecasting, and in fact, it may be. In this tutorial, you will discover how to develop an LSTM forecast model for a one-step univariate time series forecasting problem. Time Series Forecasting with the Long Short-Term Memory Network in Python Photo by Matt MacGillivray, some rights reserved. This is a big topic and we are going to cover a lot of ground. This tutorial assumes you have a Python SciPy environment installed. You can use either Python 2 or 3 with this tutorial.


Time Series Forecasting with the Long Short-Term Memory Network in Python

#artificialintelligence

The Long Short-Term Memory recurrent neural network has the promise of learning long sequences of observations. It seems a perfect match for time series forecasting, and in fact, it may be. In this tutorial, you will discover how to develop an LSTM forecast model for a one-step univariate time series forecasting problem. Time Series Forecasting with the Long Short-Term Memory Network in Python Photo by Matt MacGillivray, some rights reserved. This is a big topic and we are going to cover a lot of ground. This tutorial assumes you have a Python SciPy environment installed. You can use either Python 2 or 3 with this tutorial.