Teddy Poker

During the final heads-up match, Mike open-raises huge 20 big blinds with pocket kings, Teddy 3-bets to half of his stack big blinds — yet another huge overbet , and Mike 4-bets all-in.

He puts it back in the tray and folds, indicating he did not have the hand he was representing aces. Mike is about to bet this flop with his top two pair when Teddy goes for the Oreo-to-the-ear move again, slowly separating the cookie and savoring the treat.

So, Mike decides to check. Teddy puts out one of his usual overbets and Mike makes the big laydown. After Mike calls Teddy out for having the nuts, Teddy, in disbelief, throws his chip rack full of cookies against the wall, presumably realizing his Oreo tell as a fatal flaw.

Teddy, in general, gives off too much information at the table. A sharp rounder like Mike McDermott can pick up on that information and tear Teddy to shreds, as he does by the end of the movie.

Note: Learn step-by-step how to become the best player at the table when you join the Upswing Lab training course. We have a horrible choice between a super-shady, self-serving, lying criminal Hillary Clinton and an emotionally-unbalanced, shoot-from-the-hip authoritarian who lacks the intellectual curiosity to even want to learn the complexities that come with running a major nation like the US Donald Trump.

Where does the Iceman get his headphones? Is the watch he wears real? Niggaz with Osteoporosis.

Originally Posted by TheXFactor. I understand with Michael's legal trouble why he was unavailable to be part of The Iceman nWo but it still sucks he didn't invite Brandon and Donald to be team pros.

This time, instead of using gradient ascent to maximize a reward function, we will use gradient descent to minimize a cost function. In my last post we learned what gradient ascent is, and how we can use it to maximize a reward function.

This time, instead of using mean squared error as our reward function, we will use the Sharpe Ratio.

We can use reinforcement learning to maximize the Sharpe ratio over a set of training data, and attempt to create a strategy with a high Sharpe ratio when tested on out-of-sample data.

In the next few posts, I will be going over a strategy that uses Machine Learning to determine what trades to execute. Before we start going over the strategy, we will go over one of the algorithms it uses: Gradient Ascent.

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These talks analyze the ways a story can enhance, alter and transform how we see the world around us. There are more ways to be civically engaged besides voting.

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