The bot uses Monte Carlo simulations running from a given state. Suppose you start with 2 high cards (two Kings for example), then the chances are high that you will win. The Monte Carlo simulation then simulates a given number of games from that point and evaluates which percentage of games you will win given these cards. If another King shows during the flop, then your chance of winning will increase. The Monte Carlo simulation starting at that point, will yield a higher winning probability since you will win more games on average.
If we run the simulations, you can see that the bot based on Monte Carlo simulations outperforms the always calling bot. If you start with a stack of $100,-, you will on average end with a stack of $120,- (when playing against the always-calling bot).
It’s a start, and an opening for more sophisticated logic and analysis.
There are plenty of times when you might want to copy your SQL logins (including the SID) from one server to another. Perhaps you’re running an AG and need to make sure that all users exist on all your secondaries with the correct SID, perhaps you’re migrating servers and need all the logins on your new server. Whatever the reason, there are a number of different ways in which you can do this but they usually require scripting out on one server and then running the script into another server, or of course there’s Powershell.
The below script will use Python to copy all or specified logins from one server to another, including the password and SID.
Click through for the script. The main use case for a SQL Server DBA to learn Python as a DBA scripting language would be if you run SQL on Linux—I don’t think Powershell on Linux is far enough developed to handle the full range of DBA tasks. Otherwise, I’d use Powershell and dbatools.
You don’t have to be a data scientist to use machine learning in SQL Server. You can use pre-trained models available for usage out of the box to do your analysis. The following example shows you how you quickly get started and do text sentiment analysis.
Before starting to use this model, you need to install it. The installation is quick and instructions for installing the model can be found here: How to install the models on SQL Server
Once you have SQL Server installed with Machine Learning Services, enabled external script execution, and installed the pre-trained model, you can execute the following script to create a stored procedure that uses Python and the microsoftml function get_sentiment with the pre-trained model to determine the probability of positive sentiment of a text:
Click through to read the whole thing.
To enable data scientists to leverage the value of big data, Spark added a Python API in version 0.7, with support for user-defined functions. These user-defined functions operate one-row-at-a-time, and thus suffer from high serialization and invocation overhead. As a result, many data pipelines define UDFs in Java and Scala, and then invoke them from Python.
Vectorized UDFs built on top of Apache Arrow bring you the best of both worlds—the ability to define low-overhead, high performance UDFs entirely in Python.
This looks like a good performance improvement coming to PySpark, bringing it closer to Scala/Java performance with respect to UDFs.
In 2017 we conducted our first ever extra-large, industry-wide survey to captured the state of data science and machine learning.
As the data science field booms, so has our community. In 2017 we hit a new milestone of reaching over 1M registered data scientists from almost every country in the world. Representing many different backgrounds, skill levels, and professions, we were excited to ask our community a wide range of questions about themselves, their skills, and their path to data science. We asked them everything from “what’s your yearly salary?” to “what’s your favorite data science podcasts?” to “what barriers are faced at work?”, letting us piece together key insights about the people and the trends behind the machine learning models.
Without further ado, we’d love to share everything with you. Over 16,000 responses surveys were submitted, with over 6 full months of aggregated time spent completing it (an average response time of more than 16 minutes).
Click through for a few reports. Something interesting to me is that the top languages/tools were, in order, Python, R, and SQL. For the particular market niche that Kaggle competitions fit, that makes a lot of sense: I tend to like R more for data exploration and data cleansing, but much of that work is already done by the time you get the dataset.
Conclusion, IRIS dataset is – due to the nature of the measurments and observations – robust and rigid; one can get very good accuracy results on a small training set. Everything beyond 30% for training the model, is for this particular case, just additional overload.
The general concept here is, how small can you arbitrarily slice the data and still come up with the same result as the overall data set? Or, phrased differently, how much data do you need to collect before predictions stabilize? Read on to see how Tomaz solves the problem.
Here, we will show you how you can use the asynchronous execution mechanism offered by SQL Server Service Broker to ‘queue’ up data inside SQL Server which can then be asynchronously passed to a Python script, and the results of that Python script then stored back into SQL Server.
This is effectively similar to the external message queue pattern but has some key advantages:
- The solution is integrated within the data store, leading to fewer moving parts and lower complexity
- Because the solution is in-database, we don’t need to make copies of the data. We just need to know what data has to be processed (effectively a ‘pointer to the data’ is what we need).
Service Broker also offers options to govern the number of readers of the queue, thereby ensuring predictable throughput without affecting core database operations.
There are several interconnected parts here, and Arvind walks through the entire scenario.
Let’s define the semantic relatedness of two WordNet nouns x and y as follows:
- A = set of synsets in which x appears
- B = set of synsets in which y appears
- distance(x, y) = length of shortest ancestral path of subsets A and B
- sca(x, y) = a shortest common ancestor of subsets A and B
This is the notion of distance that we need to use to implement the
sca()methods in the
It looks like a helpful assignment for understanding natural language processing a little better.
Python has been getting some attention recently for its impressive growth in usage. Since both R and Python are used for data science, I sometimes get asked if R is falling by the wayside, or if R developers should switch course and learn Python. My answer to both questions is no.
First, while Python is an excellent general-purpose data science tool, for applications where comparative inference and robust predictions are the main goal, R will continue to be the prime repository of validated statistical functions and cutting-edge research for a long time to come. Secondly, R and Python are both top-10 programming languages, and while Python has a larger userbase, R and Python are both growing rapidly — and at similar rates.
I had a discussion about this last night. I like the language diversity: R is more statistician-oriented, whereas Python is more developer-oriented. They both can solve the same set of problems, but there are certainly cases where one beats the other. I think Python will end up being the more popular language for data science because of the number of application developers moving into the space, but for the data analysts and academicians moving to this field, R will likely remain the more interesting language.
I’m starting to experiment with Python scripts in SQL Server 2017 using Machine Learning Services (In-Database). The problem is, I don’t know Python. If I run into a Python error, the output I get from SSMS is not looking too helpful. My instincts tell me I’ll be much better off developing and debugging Python code from a development tool. What I settled on was to use Visual Studio along with the Python interpreter that comes with SQL Server 2017 Machine Learning Services. I ran into a few issues that I’ll review here.
The first thing I did was Install Python support in Visual Studio on Windows. This article from Microsoft was simple enough. It worked for me with Visual Studio Community 2015. I quickly created a “PythonApplication1” project and tried Hello World. But I got an error telling me Visual Studio couldn’t find any interpreters.
Click through to read more. With Visual Studio 2017, it’s a bit easier to get started: select the Data Science pack on installation and you’ll get both Python and R support out of the box.