From CNNs toattention mechanisms, encoding inductivebiases intoneural networks has been a fruitful source of improvement in machine learning.

However, we make no serious efforts to find the optimal architecture. In fact, we use the same 13 architecture for allour experiments, across the scales. Webelievethe performance onaparticular task can be further improved by carefully curating the neural architecture.

Now that we have an understanding of Baye's Rule, let's try to use it to analyze linear regression models. Where i is the dimensionality of the data X. Yj is the corresponding output for Xj. If i 3, Yj w1* x1j w2* x2j w3* x3j Where j is ranging from 1 to N where N is the number of data points we have. While the process of Bayesian modelling will be taken up in next part, let us consider the below model as true, for now.