In our previous blog, we saw that it is possible to provide multiple outputs, each one for a specific use case. So far, the two binary use cases, winning and winner, and the categorical use case, move, do not have any mutual dependencies between them: Their result are all derived from the original inputs. Let’s …
Continue reading ‘Tic-Tac-Toe and AI: Stacked Multi-Output Model (Part 6)’ »
As we have seen in the blog posts before, determining whether a board has a winner, determining which player the winner is, and with which move the winner won the game can all be done using neural networks. These networks only require Dense layers. However, each case has an inherent complexity, so a “minimal” number …
Continue reading ‘Tic-Tac-Toe and AI: Wrapping Up the Four Models – Multi-Output (Part 5)’ »
After having implemented neural networks for determining whether a Tic-Tac-Toe board has a winner or not, and which player the winner is, it is now time to have a look at which is the winning move. Looking at this case, you will notice that there are only three possible cases: Additionally, there is the special …
Continue reading ‘Tic-Tac-Toe and AI: And what about the Winning Move? (Part 4)’ »
After having determined if a board has a winner using TensorFlow in the previous blog post, let us tackle a very similar question: Who is the winner? Again this is a binary decision: Either X (“0”) or O (“1”) may win a board. It is also possible that we run into a tie, and therefore …
Continue reading ‘Tic-Tac-Toe and AI: Who is the Winner? (Part 3)’ »
As a first learning task for using AI with Tic-Tac-Toe, let us take the task of determining whether a board is a winning one or not (i.e. either X or O has won the game). We cannot directly tell the neural network what the rules are for an assignment to be winning. Instead, we need …
Continue reading ‘Tic-Tac-Toe and AI: A Winning Board (Part 2)’ »
You might still remember the times when you played Tic-Tac-Toe (a.k.a. noughts and crosses) in your childhood: In a series of blog posts I want to apply neural networks on this well-known game. But before we may do that, we need to do some preparations. Eventually, we want to teach a neural network to determine: …
Continue reading ‘Preparations: Tic-Tac-Toe and AI (Part 1)’ »
By default Tensorflow preallocates GPU memory eagerly. Background for that is that it wants to prevent memory fragmentation. The amount of memory it allocates is around 80% of the memory available. Although running a rather small model with less than 45k of parameters, the monitoring tool nvidia-smi shows this:
The sequel of the previous post analyzes a second setup to see if Amazon Machine Learning really “learns” – or just repeats what it has memorized.
Machine Learning today is in everybody’s mouth. Amazon Machine Learning services is said to be an offering. Politicians and traders are tasting the “next big thing” to be ahead. But does machine learning really deserve to be said to “learn”? Let’s see…
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