Actualidad Deep Learning

Artificial intelligence has moved far beyond the stuff of science fiction. And, for all the benefits AI provides today, we can only guess at what the future of artificial intelligence holds. To help ensure that they will be able to take advantage of any and all AI advancements, many companies are making use of data lakes. https://www.itprotoday.com/storage/use-data-lakes-bet-future-artificial-intelligence

https://www.kdnuggets.com/2011/02/free-public-datasets.html

Whereas data warehouses and data marts tend to force companies into narrow data paradigms and silos, data lakes emphasize a more holistic and expansive view of analytics. Data lakes deliver a more adaptive approach towards analyzing data, and stress the value of all information, instead of pre-screened bits and pieces. https://www.cio.com/article/3315660/5-reasons-why-data-lakes-are-vital-for-startup-analytics.html

In my previous posts, I discussed how to identify, acquire, cleanse, and extract meaning from Internet content and use it to build your business applications. But how do you ensure that your system always returns the highest-quality results? This is where quality analysis plays an essential role in your web data mining project. https://www.searchtechnologies.com/blog/data-mining-quality-analysis

In my previous posts, I provided the tools and techniques for selecting, extracting, cleansing, and understanding content from the Internet in order to support your business use case. In this blog, I'll discuss how to use the processed data for your own custom search, analytics, and business intelligence (BI) applications. https://www.searchtechnologies.com/blog/building-search-analytics-applications

In the first and second parts of this blog series, I discussed how to identify and acquire content from various Internet sources for your data mining needs. In this third blog, I'll provide an overview of some common techniques and tools for data cleansing and formatting. https://www.searchtechnologies.com/blog/data-cleansing-techniques-data-mining

In the first part of this blog series, I discussed how to identify the sources for your data mining needs. Once you've done that, you will need to fetch it and download it to your own computers so it can be processed. I'll cover this step here in the second part of the blog series. https://www.searchtechnologies.com/blog/web-content-extraction-data-mining

Have you ever said that sentence? In my recent experience, this sentence is coming up more and more. After all, the Internet has so much incredible information, if only it could be downloaded and processed – just think of how valuable it could be? https://www.searchtechnologies.com/blog/web-data-mining-tools-techniques

In many projects I carried out, companies, despite having fantastic AI business ideas, display a tendency to slowly become frustrated when they realize that they do not have enough data… However, solutions do exist! The purpose of this article is to briefly introduce you to some of them (the ones that are proven effective in… https://medium.com/@alexandregonfalonieri/dealing-with-the-lack-of-data-in-machine-learning-725f2abd2b92?source=email-ae8114b14513-1559097493099-digest.reader------0-59------------------8a078c29_af2e_4f47_ab31_da7f05e48097-1&sectionName=top

https://lionbridge.ai/datasets/5-million-faces-top-15-free-image-datasets-for-facial-recognition/

competición Kaggle http://diabetes.diabetesjournals.org/content/68/Supplement_1/546-P

Open datasets https://medium.com/datadriveninvestor/top-8-sources-for-machine-learning-and-analytics-datasets-5d2d94ada8ab