The tool, dubbed Simple ML, is available in beta. It's provided as an addon to Google Sheets that users can download at no charge. Simple ML was created by one of the Google teams responsible for developing TensorFlow, a popular open-source AI tool that the search giant released in 2015.

In this Python tutorial, we will discuss the error "import error no module named TensorFlow". Here we'll cover the reason related to this error using Python. And we'll also cover the following topics: In the above code, we have used the tf.add() function and within this function, we assigned the given tensors'tens1' and'tens2' as an argument. Here is the Screenshot of the following given code. Now let's see the solution for this error: If you have installed Visual code Studio then it will use a pip environment and if you want to import some needed libraries then you have to install via command.

AMPXF Boost your forum's mobile traffic with Accelerated Mobile Pages What is AMP? Accelerated Mobile Pages (AMP) load instantly Search engines prioritize these pages in search results AMP gives an average of 27.1% boost in organic traffic Addon features Creates AMP variants of relevant pages Compatible with all XF Themes Compatible with most XF addons (As long as it doesn't heavily rely on JS) Robot that continuously monitors pages for broken content Note on pricing: Xenforo resources don't seem to support price ranges. Therefore the price listed is set as the "Non-profit" license type. Note on callbacks: The addon sends a callback to ampxf.com On first install it also submits the sitemap to ampsiteindexer.com

Commercial application of facial recognition demands robustness to a variety of challenges such as illumination, occlusion, spoofing, disguise, etc. Disguised face recognition is one of the emerging issues for access control systems, such as security checkpoints at the borders. However, the lack of availability of face databases with a variety of disguise addons limits the development of academic research in the area. In this paper, we present a multimodal disguised face dataset to facilitate the disguised face recognition research. The presented database contains 8 facial add-ons and 7 additional combinations of these add-ons to create a variety of disguised face images. Each facial image is captured in visible, visible plus infrared, infrared, and thermal spectra. Specifically, the database contains 100 subjects divided into subset-A (30 subjects, 1 image per modality) and subset-B (70 subjects, 5 plus images per modality). We also present baseline face detection results performed on the proposed database to provide reference results and compare the performance in different modalities. Qualitative and quantitative analysis is performed to evaluate the challenging nature of disguise addons. The dataset will be publicly available with the acceptance of the research article. The database is available at: https://github.com/usmancheema89/SejongFaceDatabase.

A collection of real-time interactive applications and associated scripts for working with machine learning. All apps contained here require openFrameworks to run, as well as a number of addons, listed below. The openFrameworks apps are provided as source code, and can be built and compiled by using the project generator that comes with openFrameworks. Several of these applications are coupled with python scripts which do some analysis of media (feature extraction, t-SNE, etc), whose results are then imported into your ofApp via JSON or some other means for further processing. Some of them can be replicated entirely within openFrameworks, and wherever possible (for example, t-SNE) such applications are also provided.

But while we've been traditionally involved in providing you with the data that you need, we are now taking it a step further by helping you analyze it as well. To this end, we'd like to officially announce the MonkeyLearn integration for Scrapy Cloud. This feature will bring machine learning technology to the data that you extract through Scrapy and Portia. MonkeyLearn is a classifier service that lets you analyze text. It provides machine learning capabilities like categorizing products or sentiment analysis to figure out if a customer review is positive or negative.

This blog was originally featured on blog.aylien.com, a Text Analysis blog with tutorials, Data Visualisations and industry discussions. Our founder, Parsa Ghaffari, gave a talk recently on Natural Language Processing and Sentiment Analysis at the Science Gallery in Dublin. As part of the talk, he put together a nice little example of how you can transform your Google Spreadsheet into a powerful Text Analysis and Data Mining tool. In this case, he took a simple example of analyzing restaurant reviews from a popular review site but the same could be done for hotels, products, service offerings and so on. He wanted to show how easy it can be for data geeks and even the less technical marketers among us, to start analyzing text and gathering business insight from the reams of textual data online today.