anchor.diff:  
   print $"Validation loss: {validLoss/validSet.length}"
   
  -
   (**
   
  -Numerous other model definition and gradient/training patterns are supported. See the tutorials in the left-hand menu and [examples](https://github.com/DiffSharp/DiffSharp/tree/dev/examples) on GitHub.
  +Numerous other model definition, differentiation, and training patterns are supported. See the tutorials in the left-hand menu and [examples](https://github.com/DiffSharp/DiffSharp/tree/dev/examples) on GitHub.
   
   ## More Information
   
  
positive.code: 
  🗹 Common optimizers, model elements, differentiable probability distributions
  
  🗹 F# for robust functional programming 
  
  🗹 PyTorch familiar naming and idioms, efficient LibTorch C++ tensors
  
  🗹 Linux, macOS, Windows and CUDA supported
  
  🗹 Use notebooks in Jupyter and Visual Studio Code
  
  🗹 100% open source 
  
  
negative.code: 
  # DiffSharp: Differentiable Tensor Programming Made Simple
  
  DiffSharp is a tensor library with support for [differentiable programming](https://en.wikipedia.org/wiki/Differentiable_programming).
  It is designed for use in machine learning, probabilistic programming, optimization and other domains.
  
  <button class="button" style="vertical-align:middle" onclick="window.location.href='{{root}}install.html'"><span>Install »</span></button>
  