Why deep learning prefer the probability distribution with a sharp point? I am reading Ian Goodfellow's book about deep learning and when it introduces exponential distribution, it says "In the context of deep learning, we often want to have a probability distribution with a sharp point at x=0." I don't understand why the probability distribution should have this character, does it help to train the model?

 A: These two distribution have a connection with deep learning via regularisation. In deep learning we are often concerned with regularising the parameters of a neural network because neural networks tend to overfit and we want to improve ability of the model to generalise to new data. 
From a Bayesian perspective, fitting a regularised model can be interpreted as computing the maximum a posteriori (MAP) estimate given a specific prior distribution over the weights $w_i$. In particular,


*

*the $L^2$ (a.k.a. weight decay) norm  corresponds to a Gaussian prior on the weights $w$, and

*the $L^1$ norm corresponds to an isotropic Laplace prior over the weights $w$.


The $L^1$ norm (a.k.a. the Laplace prior on weights) by virtue of it's sharpness encourages sparsity (many zeros) in $w$ for reasons explained here: Why L1 norm for sparse models . This type of regularisation can be quite desirable. The connection between the Laplacian distribution and the $L^1$ norm is explained in more detail here: Why is Lasso penalty equivalent to the double exponential (Laplace) prior?
Most of what I have mentioned here is discussed in more detail in the same "Deep Learning" book in section 5.6.1 and section 7.1.2. 
