Sure thing 

The training loss goes down. This is good as it means you implemented the training correctly and that the dataset is learnable
Validation loss mostly goes down, great! It is a bit unstable though. Do you validate on the whole dataset? How big is the validation dataset?
Accuracy goes up, peaks at epoch 9 and goes down - but not that bad. I’d say completely normal

To improve

Run the experiment several times (10-15 if you have the time) and create/use a tool to see if the trainings follow this pattern repeatably
Inspect the validation data that is incorrect and compare that to correct data. Can you find any pattern  where/why it fucks up? If you are doing classification, try vizualising a confusion matrix
Has anyone attempted to solve your problem before? Can you look at their solution to inspire you further?

Just some trix, let me now if you want to discuss further