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Title | Tensorscience.com - Open source machine learning in |
Description | About Tensorscience.com is a repository of machine learning guides in Python. Free and open source, covering object recognition. OCR, NLP, and other complex challenges that benefit deeply from machine |
Keywords | N/A |
WebSite | tensorscience.com |
Host IP | 52.73.153.209 |
Location | United States |
Site | Rank |
US$767,373
Last updated: 2022-06-19 06:32:03
tensorscience.com has Semrush global rank of 13,792,917. tensorscience.com has an estimated worth of US$ 767,373, based on its estimated Ads revenue. tensorscience.com receives approximately 88,543 unique visitors each day. Its web server is located in United States, with IP address 52.73.153.209. According to SiteAdvisor, tensorscience.com is safe to visit. |
Purchase/Sale Value | US$767,373 |
Daily Ads Revenue | US$709 |
Monthly Ads Revenue | US$21,251 |
Yearly Ads Revenue | US$255,004 |
Daily Unique Visitors | 5,903 |
Note: All traffic and earnings values are estimates. |
Host | Type | TTL | Data |
tensorscience.com. | A | 299 | IP: 52.73.153.209 |
tensorscience.com. | A | 299 | IP: 34.194.149.67 |
tensorscience.com. | NS | 3600 | NS Record: dns1.p04.nsone.net. |
tensorscience.com. | NS | 3600 | NS Record: dns2.p04.nsone.net. |
tensorscience.com. | NS | 3600 | NS Record: dns3.p04.nsone.net. |
tensorscience.com. | NS | 3600 | NS Record: dns4.p04.nsone.net. |
Toggle navigation tensorscience open-source machine learning NLP Object Recognition OCR Web scraping About Contact About Tensorscience.com Tensorscience.com is a free, open source repository of practical guides on machine learning in Python. It covers complex challenges that benefit deeply from deep learning, such as optical character recognition (OCR), natural language processing (NLP) and object recognition. It aims to link the science of machine learning and artificial intelligence on the one hand (of which certain areas are still highly theoretical and out of the realms of current computing capacities) to what is currently possible computationally on the other hand. The tutorials draw upon a combination of code developed internally, open source software, and chunks of code on machine learning that are available in repositories such as GitHub and Stack Overflow, making these more accessible to the public. Latest content Introduction to Scikit-learn: classifying poisonous mushrooms |
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