No. You want to have a bigger set for training, and a smaller set for validation. The reason I think is because the more data you have to train, the better you will be able to tune your model, and then you just need a smaller validation set to check the accuracy of your model.
I don't know if there's a particular ratio that is recommended thought. For example, in the video @jeremy mentions that for the training set there where originally
12,500 images for each (cats / dogs) but then he took
1,000 for each to create the validation data set.
In the material however, on the sample data set, there are I think about
8 cats and
8 dogs in the train and
4 of each in the validation. He mentioned however that he would have rather have something like
In my particular case, to work with dogs vs cats I am using
100 cats and
100 dogs, and dividing those in
90 for training and
10 for validation.
Not related to your question, but I found this gist by @brookisme, which comes in handy when you are setting up your data sets.