Title: When I started working with neural nets I instinctively started using git. 
Soon I realised that git isn&#x27;t working for me. Working with neural nets seems way more empirical than working with a &#x27;regular&#x27; project where you have a very specific feature (e.g. login feature): you create a branch where you implement this feature. Once the feature is implemented you merge with your develop branch and you can move to another feature.<p>The same approach doesn&#x27;t work with neural nets for me. There&#x27;s &#x27;only&#x27; one feature you want to implement - you want your neural net to generalise better&#x2F;generate better images&#x2F;etc (depends on the type of problem you are solving). This is very abstract though. One often doesn&#x27;t even know what&#x27;s the solution until you empirically try to tweak several hyper parameters and see the loss function and accuracy. This makes the branch model impossible to use I think. 
Consider this: you create a branch where you want to use convolutional layers for example. Then you find out that your neural net is performing worse. What should you do know? You can&#x27;t merge this branch to your develop branch since it&#x27;s a basically &#x27;dead end&#x27; branch. On the other hand when you delete this branch you lose information that you&#x27;ve already tried this model of your net. This also produce huge amount of branches since you have enormous number of combinations for your model (e.g. convolutional layers may yield better accuracy when used with different loss function).<p>I&#x27;ve ended up with a single branch and a text file where I manually log all models I have tried so far and their performance. This creates nontrivial overhead though.
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