Sequence board game (VB6) for PC Black jack
This exposes them first to the risk of busting so they might all have busted before the dealer even gets the chance to act.
In these cases, the assumption is that the dealer will end up with a high hand total, so the players must hit or perish.
Probability of Win or Tie vs.
Neural nets are highly flexible algorithms � like soft clay, a neural net adjusts itself to fit the contours of the data even with little to no transformation.
Data that would trouble something more rigid like linear regression is easily handled by a visual basic 6 blackjack net.
Additionally, the layers and neurons within the network will learn any deeply embedded, article source relationships that may exist in the data.
However, this versatility comes at a cost � the neural net is a black box model.
Unlike regression where we can learn how the model makes decisions by looking at the regression coefficients, there is no such transparency with a neural net.
Also, neural nets run the risk of fitting our data too well and then not generalizing well on out of https://nycwebdesigner.org/blackjack/holiday-inn-fargo-blackjack.html data.
In my opinion, these disadvantages are worth keeping in mind and designing safeguards for, but they are not reasons to shy away from using neural nets.
Generating Our Training Data Before we can train our neural net, we first need to figure out how to structure our training data so that the model we build with it will be useful.
What do we want to predict?
Given the situation, we might want the model to tell us what the probability of a loss is.
Then again, this would only be useful if we could scale up or down our bet, which we cannot in blackjack.
It actually took me a while to figure out the best way to set this up.
But here is what I came up with.
We need a way for the neural net to know whether a given move was correct or not.
So my method of deciding whether a given move is the correct one is to simulate a game of blackjack: deal the cards to both player and dealer, check if anyone has a blackjack, make only one move either hit or staysimulate the game to its end and record the result.
This allows us to train our model so that its output is a prediction of whether to hit or stay.
And the target variable is the correct decision as defined by the logic above.
Training the Neural Net We will be using the for our neural net.
The first line line 1 creates a sequential type neural net, which is a linear sequence of neural net layers.
The lines after line 1 add layers to our model one by one dense is the simplest layer type and link just a bunch of neurons � the numbers like 16, 128, etc.
Finally for the last layer, we need to choose an activation function.
This converts the raw output of the neural network into something interpretable by us.
Pay attention to two things about the final layer.
First, it includes only one neuron because we are predicting between two possible outcomes two class problem.
The last two lines tell our neural net model what loss function to use is a loss function used by classification models that output probabilities and fits the model to our data.
The plot below shows the ROC Curve of our blackjack playing neural net � the neural net seems to be adding a fair bit of value over guessing randomly the red dashed line.
Its area under the curve, or AUC, of playtech strategy />ROC Curve for Our Blackjack Playing Neural Net I used my training data to plot the ROC Curve.
Usually we would want to plot it using our validation or test data, but in this visual basic 6 blackjack we know that as long as our sample is big enough, then it is representative of the population assuming we keep playing blackjack with the same rules.
And we would expect our model to generalize well any new data would have the same underlying statistical characteristics as our training data.
Before our neural net can officially start gambling, we need to give it a decision rule.
Remember that the sigmoid activation from our final neural net layer makes our neural network output a probability that the correct move is to hit.
We need a decision rule, where given this probability, we decide whether to hit or stay.
So when it comes time to decide what visual basic 6 blackjack do, the neural net will make its decision based on the card that the dealer is showing, the total hand value of its own cards, and whether or not it is holding an ace.
Our Model is Pretty Good!
The following table shows the outcome distribution for each strategy type.
Two things flash blackjack embedded out to me.
First, our neural net only lost slightly less than half 49% the games it played.
Outcome Breakdown by Strategy We can also take a look at how the strategies perform across our key features dealer card and player hand total.
In the plot below, if the dealer is showing a low card, our neural network performs about as well as the naive strategy.
But when the dealer is showing a higher card 7 or moreour neural net performs significantly better.
Probability of Tie or Win vs.
This looks pretty promising � our neural net performs as well or better across the board.
And unlike the naive strategy, which performs even worse than random guessing in The Valley of Despair player hand values between 12 and 16our neural network performs better.
The most recent plot hints at how the neural net is able to surpass the naive strategy.
The naive strategy because of how we coded it is unwilling to take a chance any time that there is even a remote risk of busting.
The neural net, on the other hand regularly hits on 12s, 13s, 14s, or 15s.
It looks like there is a strong preference to hit when the dealer is showing a high card 8, 9, or 10.
But even when the dealer is showing a low card like 3, the neural net still chooses to hit 60% of the time � this is because the neural net is visual basic 6 blackjack into account all the features that it has at its disposal when making a decision.
If it might vary greatly, then a statistical model might not even be the right answer to your problem.
And at the very least you must be cognizant of that and build in safeguards such as regularization and rigorous as well as honest validation and blackjack codes vegas set benchmarking article source your model.
Finally a last word on blackjack.
Hope you found this as interesting as I did.
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How to Play (and Win) at Blackjack: The Expert's Guide
Basic Strategy is the first step to beating blackjack with card counting.. In case you're not a visual learner these simple phrases might help you commit these rules to memory.. A pair of 6's splits against dealer 2 through 6, otherwise hit.
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