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How did I create a Blackjack game with Python in a few hours

This allows us to train our model so that its output is a prediction of whether to hit or stay. Building a Simple UI for Python.{/INSERTKEYS}{/PARAGRAPH} Here is a quick recap of our previous findings:. The neural net, on the other hand regularly hits on 12s, 13s, 14s, or 15s. 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. The lines of code to actually instantiate and train our neural net are pretty simple. However, this versatility comes at a cost β€” the neural net is a black box model. 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. Its area under the curve, or AUC, of 0. James Briggs in Towards Data Science. The most recent plot hints at how the neural net is able to surpass the naive strategy. And the target variable is the correct decision as defined by the logic above. Get this newsletter. See responses 1. Rather, we want our neural net to identify the correct action, hit or stay. Make learning your daily ritual. {PARAGRAPH}{INSERTKEYS}L ast time we developed code to simulate blackjack. So when it comes time to decide what to 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. The random strategy is to flip a coin β€” if it comes up heads hit, otherwise stay. To remind everyone: I ran approximately , blackjack simulations for each strategy type neural net, naive, and random. Whether the player has an ace or not. I used my training data to plot the ROC Curve. In my view, there are two candidates for our target variable:. Time to Play! The ROC Curve tells us how good our model is at trading off between benefit True Positive Rate and cost False Positive Rate β€” the greater the area under the curve is, the better the model. This looks pretty promising β€” our neural net performs as well or better across the board. Then again, this would only be useful if we could scale up or down our bet, which we cannot in blackjack. Here are a few things to keep in mind when you are training your own models whether they be decisions trees, regressions, or neural nets :. Before our neural net can officially start gambling, we need to give it a decision rule. The last two lines tell our neural net model what loss function to use binary cross-entropy is a loss function used by classification models that output probabilities and fits the model to our data. Our Model is Pretty Good! But the primary features are:. 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 stay , simulate the game to its end and record the result. 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. It actually took me a while to figure out the best way to set this up. Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. The plot below shows the ROC Curve of our blackjack playing neural net β€” the neural net seems to be adding a fair bit of value over guessing randomly the red dashed line. Thus, using 0. The lines after line 1 add layers to our model one by one dense is the simplest layer type and is just a bunch of neurons β€” the numbers like 16, , etc. 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. 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. Towards Data Science Follow. Code up and train the neural net to play blackjack hopefully optimally. We will:. Now we just need to add the above function to our code where we decide whether or not to hit please refer to my GitHub if you are curious how I coded this part. But if someone were interested in moving forward with or without my code, here are a few potentially interesting extensions to this project:. Are Data Scientists at Risk of Automation. My 10 favorite resources for learning data science online. Data that would trouble something more rigid like linear regression is easily handled by a neural net. If you are unfamiliar with the game of blackjack, my previous post also describes how the game is played and the rules. This converts the raw output of the neural network into something interpretable by us. And we would expect our model to generalize well any new data would have the same underlying statistical characteristics as our training data. 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. Additionally, the layers and neurons within the network will learn any deeply embedded, non-linear relationships that may exist in the data. Neural nets are highly flexible algorithms β€” like soft clay, a neural net adjusts itself to fit the contours of the data even with little to no transformation. The naive strategy is to only hit when there is zero chance of busting hit for hand totals below 12, and stay for hand totals of 12 or more. Erik van Baaren in Towards Data Science. Making Data Science Interviews Better. Richmond Alake in Towards Data Science. But here is what I came up with. The action of the player hit or stay. Usually we would want to plot it using our validation or test data, but in this case 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. Hope you found this as interesting as I did. In the plot below, if the dealer is showing a low card, our neural network performs about as well as the naive strategy. Training the Neural Net We will be using the Keras library for our neural net. We need a decision rule, where given this probability, we decide whether to hit or stay. Two things jump out to me. Sign in. Finally for the last layer, we need to choose an activation function. A Medium publication sharing concepts, ideas, and codes. To remind everyone:. More From Medium. Tony Yiu Follow. We need a way for the neural net to know whether a given move was correct or not. Create a free Medium account to get The Daily Pick in your inbox. We will: Generate data using our blackjack simulator that we coded last time with a few modifications to make it more suitable for training algorithms. Pay attention to two things about the final layer. Towards Data Science A Medium publication sharing concepts, ideas, and codes. A quick way to eye-ball whether our model adds any value is to use a ROC Curve check out the linked blog by yours truly if you would like a deep dive on ROC Curves. But when the dealer is showing a higher card 7 or more , our neural net performs significantly better. What do we want to predict? Also, neural nets run the risk of fitting our data too well and then not generalizing well on out of sample data. The following table shows the outcome distribution for each strategy type. Given the situation, we might want the model to tell us what the probability of a loss is. A selection of my recent posts that I hope you will check out:. Eryk Lewinson in Towards Data Science. The first line line 1 creates a sequential type neural net, which is a linear sequence of neural net layers. We will be using the Keras library for our neural net. First, it includes only one neuron because we are predicting between two possible outcomes two class problem. In my view, there are two candidates for our target variable: Probability of losing the game. I use 0. And unlike the naive strategy, which performs even worse than random guessing in The Valley of Despair player hand values between 12 and 16 , our neural network performs better. Since the simulated player only makes a single decision, we can assess the quality of that decision by whether he wins or loses the game :. It looks like there is a strong preference to hit when the dealer is showing a high card 8, 9, or Hopefully this post gave you a decent introduction of how machine learning can be used aid real-life decision making. Finally a last word on blackjack. Harshit Tyagi in Towards Data Science. We can also take a look at how the strategies perform across our key features dealer card and player hand total. Written by Tony Yiu Follow.