1996 Grand Prix Hassan II – Singles

Gilbert Schaller was the defending champion but lost in the final 7–5, 1–6, 6–2 against Tomás Carbonell.

Singles
1996 Grand Prix Hassan II
Champion Tomás Carbonell
Runner-up Gilbert Schaller
Final score7–5, 1–6, 6–2

Seeds
  1. Gilbert Schaller (Final)
  2. Bohdan Ulihrach (First Round)
  3. Carlos Costa (Second Round)
  4. Jiří Novák (Quarterfinals)
  5. Alberto Berasategui (Semifinals)
  6. Jordi Burillo (Second Round)
  7. n/a
  8. Jordi Arrese (First Round)

Draw

Key

Finals
Semifinals Final
          
1 Gilbert Schaller 710 7
5 Alberto Berasategui 68 5
1 Gilbert Schaller 5 6 2
  Tomás Carbonell 7 1 6
  Tomás Carbonell 6 4 6
  Andrei Chesnokov 4 6 1

Top Half
First Round Second Round Quarter-finals Semi-finals
1 G Schaller 2 6 6
LL T Champion 6 1 1 1 G Schaller 7 63 6
  O Gross 2 5     M-K Goellner 5 77 4
  M-K Goellner 6 7   1 G Schaller 4 6 6
  R Fromberg 6 6     R Fromberg 6 4 2
  J Golmard 0 4     R Fromberg 6 6  
  J Cunha e Silva 6 6     J Cunha e Silva 4 4  
8 J Arrese 2 4   1 G Schaller 710 7  
4 J Novák 6 6   5 A Berasategui 68 5  
  S Noszály 1 2   4 J Novák 77 6  
Q K Goossens 6 3 4 Q F Maggi 64 3  
Q F Maggi 3 6 6 4 J Novák 3 4  
  F Santoro 2 65   5 A Berasategui 6 6  
WC H Arazi 6 77   WC H Arazi 1 3  
  M Filippini 2 3   5 A Berasategui 6 6  
5 A Berasategui 6 6  

Bottom Half
First Round Second Round Quarter-finals Semi-finals
6 J Burillo 6 6  
  C Ruud 2 1   6 J Burillo 6 4 1
  D Rikl 6 62 0   T Carbonell 3 6 6
  T Carbonell 4 77 6   T Carbonell 6 6  
  M Norman 6 77     M Norman 3 2  
  P Baur 3 61     M Norman 6 64 6
Q J A Marín 1 1   3 C Costa 4 77 2
3 C Costa 6 6     T Carbonell 6 4 6
LL T El-Sawy 6 6     A Chesnokov 4 6 1
  N Marques 1 3   LL T El-Sawy 3 0  
  A Chesnokov 6 3 6   A Chesnokov 6 6  
  J Palmer 1 6 4   A Chesnokov 6 6  
Q R Carretero 4 2     C Moyá 4 3  
  F Mantilla 6 6     F Mantilla 1 68  
  C Moyá 7 6     C Moyá 6 710  
2 B Ulihrach 5 0  
gollark: The image is just 3 matrices of R/G/B values.
gollark: There are 129057189471894718247141491807401825701892912 random details and things but that's the gist of it.
gollark: Then, you just move it a little bit toward lower loss (gradient descent).
gollark: You have a big thing of settable parameters determining how you go from input to output. And if you know what the result *should* be (on training data), then as the maths is all "differentiable", you can differentiate it and get the gradient of loss wrt. all the parameters.
gollark: Well, you put your data into something something linear algebra and something something gradient descent, and answers come out.

References
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