- After watching contestants keep their fingers attached to a phone screen for more than 70 hours, YouTube creator Jimmy “MrBeast” Donaldson called an early end to a massive competition originally designed with one winner and a $25,000 prize in mind.
- Donaldson’s “Finger on the App” was a one-time game designed in partnership with internet collective MSCHF.
- Despite Donaldson’s calls for players to “go to sleep,” a couple contestants kept going.
- One player argued on a livestream that Donaldson’s tweet could be a trick; if three of the final four players removed their fingers, the last person standing could still stand to win $25,000.
- Prior to the game’s launch, Donaldson spoke to The Verge about expectations both his team and MSCHF had for the game.
- By the end, it became clear that Donaldson wanted to control the game a little to make sure contestants finally got some sleep.

- Not so recently, a brilliant and ‘original’ idea suddenly struck me — what if I could predict stock prices using Machine Learning.
- The following piece of code downloads stock price data for Reliance over 15 years with a resolution of 1 day and stores it in a pandas dataframe.
- Let’s fix our problem statement now — the LSTM model shall see the close prices for the last 10 days (called the time_step) and predict the close price for the next day.
- But in a practical scenario, the test data will be in real-time, so you won’t know the minimum or maximum or average values beforehand!
- Finally, let’s structure the data so that our LSTM model can easily read them.
- The simple sequential model has an LSTM layer followed by a dropout (to reduce over-fitting) and a final dense layer (our output prediction).

- Not so recently, a brilliant and ‘original’ idea suddenly struck me — what if I could predict stock prices using Machine Learning.
- The following piece of code downloads stock price data for Reliance over 15 years with a resolution of 1 day and stores it in a pandas dataframe.
- Let’s fix our problem statement now — the LSTM model shall see the close prices for the last 10 days (called the time_step) and predict the close price for the next day.
- But in a practical scenario, the test data will be in real-time, so you won’t know the minimum or maximum or average values beforehand!
- Finally, let’s structure the data so that our LSTM model can easily read them.
- The simple sequential model has an LSTM layer followed by a dropout (to reduce over-fitting) and a final dense layer (our output prediction).

- Not so recently, a brilliant and ‘original’ idea suddenly struck me — what if I could predict stock prices using Machine Learning.
- The following piece of code downloads stock price data for Reliance over 15 years with a resolution of 1 day and stores it in a pandas dataframe.
- Let’s fix our problem statement now — the LSTM model shall see the close prices for the last 10 days (called the time_step) and predict the close price for the next day.
- But in a practical scenario, the test data will be in real-time, so you won’t know the minimum or maximum or average values beforehand!
- Finally, let’s structure the data so that our LSTM model can easily read them.
- The simple sequential model has an LSTM layer followed by a dropout (to reduce over-fitting) and a final dense layer (our output prediction).

- Tricia Hurt, her husband Brian, and their son Brady were out fishing on Marsh-Miller Lake on Sunday when they came across the distressed black bear.
- The family came up behind the bear, and Brian pulled on the tub but lost his grip -- but he realized the bear's ear was loose.
- Hurt said the whole ordeal lasted about five minutes.
- When they got back to the resort bar, word of their courageous act had already spread thanks to some campers watching on the shore.
- The Hurts learned from people at the bar that the bear had been running around with the tub on its head for three to four days.
- The Wisconsin Department of Natural Resources had been contacted several times but had been unable to locate it thanks to the rural, wooded landscape, Hurt said.

- Finally, let’s structure the data so that our LSTM model can easily read them.

- This is still not great because there is no guarantee that they will intersect and even then, what we are actually looking for is the range of times during which the shapes intersect so we can compute the maximum area of overlap.
- Seeing this go back and confirm the key point above that if the polygons intersect their Minkowski difference contains the origin.
- We can also compute the first and last points of intersection of these polygons using a single ray from the origin in the direction of the relative velocities of the polygons.
- Finally putting all the pieces together we have an algorithm that takes the Minkowski difference of two polygons then computes the (generally) two points of intersection of the ray from the origin to the Minkowski difference polygon.