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Monday, 13 July 2020

Show HN: Login with Matrix https://bit.ly/3elilli

Show HN: Login with Matrix https://bit.ly/2WjAGJs July 13, 2020 at 05:54PM

Lady calls out popular vendor, Prettiwoman for selling fake N35,000 sandals to her, says she used her 2months salary to buy it (Video)

A Nigerian lady identified as Brown Shuga on Instagram has taken to the platform to call out popular fashion vendor, Prettiwomann for selling fake sandals to her. According to Brown Shuga, she ordered the sandals from prettiwoman some months ago and after a short time, the sandals started peeling off. She said all efforts to […]

The post Lady calls out popular vendor, Prettiwoman for selling fake N35,000 sandals to her, says she used her 2months salary to buy it (Video) appeared first on Best9jamusic.

source https://www.best9jamusic.com.ng/entertainment/lady-calls-out-popular-vendor-prettiwoman-for-selling-fake-n35000-sandals-to-her-says-she-used-her-2months-salary-to-buy-it-video/

EFCC begs court to deny influencer, Adeherself bail after spending 6 days behind bars

The Economic and Financial Crimes Commission, (EFCC) has pleaded with an Ikeja Special Offences Court to deny popular social media influencer, Adeherself bail. She was arraigned before Justice Sherifat Solebo of the Special Offence Court sitting in Ikeja, Lagos on a one-count charge of possession of fraudulent documents Ade was among 5 persons picked up […]

The post EFCC begs court to deny influencer, Adeherself bail after spending 6 days behind bars appeared first on Best9jamusic.

source https://www.best9jamusic.com.ng/entertainment/efcc-begs-court-to-deny-influencer-adeherself-bail-after-spending-6-days-behind-bars/

My wife still cooks my meals and serves me herself – Pastor Adeboye

The General Overseer of the Redeemed Christian Church of God, RCCG, Pastor Enoch Adeboye never misses an opportunity to pour encomium on his wife, Pastor Foluke Adeboye. Pastor Adeboye, fondly called ‘daddy G.O’ is celebrating his wife’s 72nd birthday today, July 13th, and he has take to Instagram to write her sweet words. The cleric […]

The post My wife still cooks my meals and serves me herself – Pastor Adeboye appeared first on Best9jamusic.

source https://www.best9jamusic.com.ng/entertainment/my-wife-still-cooks-my-meals-and-serves-me-herself-pastor-adeboye/

Show HN: Learn coding by building 3D structures https://bit.ly/3j20WS2

Show HN: Learn coding by building 3D structures https://bit.ly/2Wdl4qA July 13, 2020 at 05:43PM

Show HN: A Twitter Clone (Hobby Project) https://bit.ly/38PVY6v

Show HN: A Twitter Clone (Hobby Project) https://bit.ly/2ZpdkUz July 13, 2020 at 03:51PM

Show HN: Fw – faster workspace (workspace productivity booster) https://bit.ly/2CsuBmW

Show HN: Fw – faster workspace (workspace productivity booster) https://bit.ly/2ZYZPKc July 13, 2020 at 03:43PM

Launch HN: Aquarium (YC S20) – Improve Your ML Dataset Quality https://bit.ly/3gVJtch

Launch HN: Aquarium (YC S20) – Improve Your ML Dataset Quality Hi everyone! I’m Peter from Aquarium ( https://bit.ly/2ATWWSy ). We help deep learning developers find problems in their datasets and models, then help fix them by smartly curating their datasets. We want to build the same high-power tooling for data curation that sophisticated ML companies like Cruise, Waymo, and Tesla have and bring it to the masses. ML models are defined by a combination of code and the data that the code trains on. A programmer must think hard about what behavior they want from their model, assemble a dataset of labeled examples of what they want their model to do, and then train their model on that dataset. As they encounter errors in production, they must collect and label data for the model to train on to fix these errors, and verify they're fixed by monitoring the model’s performance on a test set with previous failure cases. See Andrej Karpathy’s Software 2.0 article ( https://bit.ly/2C64Okw ) for a great description of this workflow. My cofounder Quinn and I were early engineers at Cruise Automation (YC W14), where we built the perception stack + ML infrastructure for self driving cars. Quinn was tech lead of the ML infrastructure team and I was tech lead for the Perception team. We frequently ran into problems with our dataset that we needed to fix, and we found that most model improvement came from improvement to a dataset’s variety and quality. Basically, ML models are only as good as the datasets they’re trained on. ML datasets need variety so the model can train on the types of data that it will see in production environments. In one case, a safety driver noticed that our car was not detecting green construction cones. Why? When we looked into our dataset, it turned out that almost all of the cones we had labeled were orange. Our model had not seen many examples of green cones at training time, so it was performing quite badly on this object in production. We found and labeled more green cones into our training dataset, retrained the model, and it detected green cones just fine. ML datasets need clean and consistent data so the model does not learn the wrong behavior. In another case, we retrained our model on a new batch of data that came from our labelers and it was performing much worse on detecting “slow signs” in our test dataset. After days of careful investigation, we realized it was due to a change to our labeling process that caused our labelers to label many “speed limit signs” as “slow signs,” which was confusing the model and causing it to perform badly on detecting “slow signs.” We fixed our labeling process, did an additional QA pass over our dataset to fix the bad labels, retrained our model on the clean data, and the problems went away. While there’s a lot of tooling out there to debug and improve code, there’s not a lot of tooling to debug and improve datasets. As a result, it’s extremely painful to identify issues with variety and quality and appropriately modify datasets to fix them. ML engineers often encounter scenarios like: Your model’s accuracy measured on the test set is at 80%. You abstractly understand that the model is failing on the remaining 20% and you have no idea why. Your model does great on your test set but performs disastrously when you deploy it to production and you have no idea why. You retrain your model on some new data that came in, it’s worse, and you have no idea why. ML teams want to understand what’s in their datasets, find problems in their dataset and model performance, and then edit / sample data to fix these problems. Most teams end up building their own one-off tooling in-house that isn’t very good. This tooling typically relies on naive methods of data curation that are really manual and involve “eyeballing” many examples in your dataset to discover labeling errors / failure patterns. This works well for small datasets but starts to fail as your dataset size grows above a few thousand examples. Aquarium’s technology relies on letting your trained ML model do the work of guiding what parts of the dataset to pay attention to. Users can get started by submitting their labels and corresponding model predictions through our API. Then Aquarium lets users drill into their model performance - for example, visualize all examples where we confused a labeled car for a pedestrian from this date range - so users can understand the different failure modes of a model. Aquarium also finds examples where your model has the highest loss / disagreement with your labeled dataset, which tends to surface many labeling errors (ie, the model is right and the label is wrong!). Users can also provide their model's embeddings for each entry, which are an anonymized representation of what their model “thought” about the data. The neural network embeddings for a datapoint (generated by either our users’ neural networks or by our stable of pretrained nets) encode the input data into a relatively short vector of floats. We can then identify outliers and group together examples in a dataset by analyzing the distances between these embeddings. We also provide a nice thousand-foot-view visualization of embeddings that allows users to zoom into interesting parts of their dataset. ( https://youtu.be/DHABgXXe-Fs?t=139 ) Since embeddings can be extracted from most neural networks, this makes our platform very general. We have successfully analyzed dataset + models operating on images, 3D point clouds from depth sensors, and audio. After finding problems, Aquarium helps users solve them by editing or adding data. After finding bad data, Aquarium integrates into our users’ labeling platforms to automatically correct labeling errors. After finding patterns of model failures, Aquarium samples similar examples from users’ unlabeled datasets (green cones) and sends those to labeling. Think about this as a platform for interactive learning. By focusing on the most “important” areas of the dataset that the model is consistently getting wrong, we increase the leverage of ML teams to sift through massive datasets and decide on the proper corrective action to improve their model performance. Our goal is to build tools to reduce or eliminate the need for ML engineers to handhold the process of improving model performance through data curation - basically, Andrej Karpathy’s Operation Vacation concept ( https://youtu.be/g2R2T631x7k?t=820 ) as a service. If any of those experiences speak to you, we’d love to hear your thoughts and feedback. We’ll be here to answer any questions you might have! July 13, 2020 at 04:05PM

Show HN: A Simple Search Engine https://bit.ly/2OjqTym

Show HN: A Simple Search Engine https://bit.ly/3fwbXcw July 13, 2020 at 03:58PM

Show HN: Income/savings calculator for moving to Canada https://bit.ly/2Zmo3iG

Show HN: Income/savings calculator for moving to Canada https://bit.ly/3erTlJb July 13, 2020 at 03:47PM

Show HN: Simple Google Login in Go https://bit.ly/2Dvhth8

Show HN: Simple Google Login in Go https://bit.ly/2OiNlaT July 13, 2020 at 11:35AM

Show HN: Soup.io Downloader https://bit.ly/3fqNiFU

Show HN: Soup.io Downloader https://bit.ly/32eqZQk July 13, 2020 at 10:11AM

Show HN: Primo – all-in-one IDE, CMS, component library, static site generator https://bit.ly/32aAlfO

Show HN: Primo – all-in-one IDE, CMS, component library, static site generator https://bit.ly/2OmurzW July 13, 2020 at 01:51PM

Show HN: A thread hierarchy management library in C https://bit.ly/32bmHcb

Show HN: A thread hierarchy management library in C https://bit.ly/3fvC1Va July 13, 2020 at 01:21PM

Sunday, 12 July 2020

Show HN: Computer Vision Boilerplate (CVB) https://bit.ly/38UeTx4

Show HN: Computer Vision Boilerplate (CVB) https://bit.ly/329mgiI July 10, 2020 at 06:05PM

SZA Narrates How She Caught Her Man Cheating on Her with Her Friend; Keke Palmer Comes in with the Plot Twist

SZA has recounted the heartbreaking experience of catching her man cheating on her with her home girl. The singer who trended on social media a few days ago after she crowned herself queen of R’n’B shared the tale in a Twitter thread. SZA revealed that she had been invited to a party by this particular friend […]

The post SZA Narrates How She Caught Her Man Cheating on Her with Her Friend; Keke Palmer Comes in with the Plot Twist appeared first on Best9jamusic.

source https://www.best9jamusic.com.ng/entertainment/sza-narrates-how-she-caught-her-man-cheating-on-her-with-her-friend-keke-palmer-comes-in-with-the-plot-twist/

Everything That Went Down On Week Six Of BBNaija Reunion

It’s been another exciting and intense week of Big Brother Naija. The tension, the laughs, and the forgiveness this week was amped up because the show ran for one hour instead of the usual thirty minutes from the previous weeks. It would continue to run for one hour when it returns next week Monday. This […]

The post Everything That Went Down On Week Six Of BBNaija Reunion appeared first on Best9jamusic.

source https://www.best9jamusic.com.ng/entertainment/everything-that-went-down-on-week-six-of-bbnaija-reunion/

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