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Title | DSGeek |
Description | DSGeek Posts Topics Projects About February 28, 2019 Kaggle Toxic Comments Competition One of my goals for 2019 is to resume competing in Kaggle competiti |
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WebSite | dsgeek.com |
Host IP | 52.216.76.35 |
Location | United States |
Site | Rank |
US$293,546
Last updated: 2023-05-13 18:39:14
dsgeek.com has Semrush global rank of 36,056,789. dsgeek.com has an estimated worth of US$ 293,546, based on its estimated Ads revenue. dsgeek.com receives approximately 33,871 unique visitors each day. Its web server is located in United States, with IP address 52.216.76.35. According to SiteAdvisor, dsgeek.com is safe to visit. |
Purchase/Sale Value | US$293,546 |
Daily Ads Revenue | US$271 |
Monthly Ads Revenue | US$8,129 |
Yearly Ads Revenue | US$97,548 |
Daily Unique Visitors | 2,259 |
Note: All traffic and earnings values are estimates. |
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dsgeek.com. | A | 300 | IP: 52.216.76.35 |
dsgeek.com. | NS | 86400 | NS Record: ns-71.awsdns-08.com. |
dsgeek.com. | NS | 86400 | NS Record: ns-1301.awsdns-34.org. |
dsgeek.com. | NS | 86400 | NS Record: ns-2021.awsdns-60.co.uk. |
dsgeek.com. | NS | 86400 | NS Record: ns-705.awsdns-24.net. |
DSGeek Posts Topics Projects About February 28, 2019 Kaggle Toxic Comments Competition One of my goals for 2019 is to resume competing in Kaggle competitions. Thinking about this made me realize that I never posted about my solution for last year’s Toxic Comment Challenge. The competition’s goal was to train a model to detect toxic comments like threats, obscenity, insults, and identity-base hate. The data set consisted of comments from Wikipedia’s talk page edits. The training data set had ~500K examples each with one, or more, of the following labels: Read More… Tags: NLP , Kaggle , TensorFlow , DeepLearning February 19, 2018 Creating TF-IDF Weighted Word Embeddings Although I’m late to the start, I’ve been working on the Kaggle Toxic Comment Challenge. The dataset only contains about 560K comments. Before trying a deep learning model, I was curious to see how well a relatively simple approach might do. At the same time, I still wanted to use word embeddings to maximize |
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