- #1
Vrbic
- 400
- 18
Hello,
I use Wolfram Mathematica (WM) for my calculation but I'm not much familiar with neural networks, machine learning, etc. On the other hand, I know that WM includes such tools. I would like to learn this stuff just for operating on this level. I read some tutorials and I would like to try some practical problem.
1) What do you mean about these tools in WM? Is it reasonable to use for practical problems or it is just some "game"?
2) I would like to try to apply these tools to predict tennis matches. I read something about functions (I write function names with the first capital letter): Predict, Classify, TimeSeriesForecast, etc. But practically I need some combination of these functions. I have enough data, the results of many players. I suggested input of learning data like (vector_palyer1),(vector_player2) -> (result) or something like that and then for prediction input (vectro_player1),(vector_player2) and output (results). I would like to predict "a future", learn from previous results. Not just classify data.
a) This input is definitely not good, because if I always give the winner as "player1", I assume that the network will learn to evaluate the first input as the winner. Sorting a player randomly doesn't come as a good idea. Does exist some good idea or function for such case?
b) Is such a problem reasonably solvable by WM?
Thank you for all comments or suggestions.
I use Wolfram Mathematica (WM) for my calculation but I'm not much familiar with neural networks, machine learning, etc. On the other hand, I know that WM includes such tools. I would like to learn this stuff just for operating on this level. I read some tutorials and I would like to try some practical problem.
1) What do you mean about these tools in WM? Is it reasonable to use for practical problems or it is just some "game"?
2) I would like to try to apply these tools to predict tennis matches. I read something about functions (I write function names with the first capital letter): Predict, Classify, TimeSeriesForecast, etc. But practically I need some combination of these functions. I have enough data, the results of many players. I suggested input of learning data like (vector_palyer1),(vector_player2) -> (result) or something like that and then for prediction input (vectro_player1),(vector_player2) and output (results). I would like to predict "a future", learn from previous results. Not just classify data.
a) This input is definitely not good, because if I always give the winner as "player1", I assume that the network will learn to evaluate the first input as the winner. Sorting a player randomly doesn't come as a good idea. Does exist some good idea or function for such case?
b) Is such a problem reasonably solvable by WM?
Thank you for all comments or suggestions.
