He defines it pretty clearly. Logits are the inputs to a softmax layer / calculation, which turn the logits into normalized percentages (the percentages sum to 1.0).

Before going through the softmax layer, the logits will be small numbers around 0, probably. Something like: [2.89, -4.53, 0.24, -1.556, 0.57]. Logits like this are natural outputs of a neural network, because they can be any real number and everything will still work.

The logits become percentage as follows:

    julia> x = [2.89, -4.53, 0.24, -1.556, 0.57]
    5-element Vector{Float64}:
      2.89
     -4.53
      0.24
     -1.556
      0.57
    
    julia> x = e.^x
    5-element Vector{Float64}:
     17.993309601550315
      0.010780676072743085
      1.2712491503214047
      0.2109782988178321
      1.768267051433735
    
    julia> x / sum(x)
    5-element Vector{Float64}:
     0.8465613320288766
     0.0005072164987105474
     0.05981058503789324
     0.009926248902037537
     0.08319461753248213

Logits is an overloaded term though, and means different things in different contexts.