How to Improve the Relationship Between UX and Development Teams in an Agile Context - Part 2

In the first part of this series, we discussed the challenges of integrating UX activities into an Agile context mainly populated by engineers (or, to use Scrum's terminology, developers). In this second part, we'll discuss various approaches to accommodating UX activity inside a Scrum framework.

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Understanding the Basics of Neural Networks

 For some years now, machine learning has been one of the most celebrated and explored areas of technological development. In particular, deep learning and neural networks have fascinating potential. To illustrate how neural networks function and to demonstrate their success, we'll explain a naive implementation of a simple neural network using Java.

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API Autodiscovery in Anypoint Platform 2

Introduction

With the release of Anypoint Platform 2, there are some exciting changes in the API Autodiscovery feature. Here in this short article, I am going to describe how to make use of it in your applications.

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Using Deep Convolutional Neural Networks (DCNNs) for Time Series Forecasting Using Tensorflow - Part 3

In part 1 of this tutorial, we explained the advantages of and proposed a methodology for using DCNNs for time series analysis by converting time series into gray-scale images. In part 2, we defined a Python class and various methods to perform data processing. In our third and final part, we will explain the topology of our model.

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Using Deep Convolutional Neural Networks (DCNNs) for Time Series Forecasting Using Tensorflow - Part 2

In our last post, we discussed the importance of developing a strong forecasting engine to predict future energy consumption based on data from past energy consumption. We then discussed the suitability of using a Deep Convolutional Neural Network (DCNN) to form accurate predictions and examined how to use this model by converting a series of load data into images.

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Using Deep Convolutional Neural Networks (DCNNs) for Time Series Forecasting Using Tensorflow - Part 1

Have you ever wondered how to convert a prediction problem into a new format so that you can solve it using available strong forecasting engines? In Part I of this tutorial, I will discuss how to solve one of the most challenging forecasting problems--the next state forecasting trend of electricity consumption--by using a Deep Convolutional Neural Network (DCNN) to process a series of load data that’s been converted into images.

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