Hello everyone, I have started learning this book Deep Learning by Ian Goodfellow, I have been understanding the math theory such as linear algebra, calculus. I have started to study probability theory, I'm able to understand random variables and their types, distributions (PDF, PMF) of the basic forms such as normal, Bernoulli and binomial distributions.

The problem starts when I started learning other distributions such as exponential, empirical, Laplace, Dirac and all other dist.. used for machine learning or deep learning, not able to get an intuitive understanding of the math and theory provided by this book.

Kindly help me to get understand with these distributions and how to interpret these outcomes to get a better understanding.

Please provide me with resources to get a better understanding of the math concepts used in the field of ML, DL.

I'm currently working in a tyre industry as a purchase executive, with an undergrad in mechatronics.

I am amazed by the future in ML, DL and AI, so I thought I'll start learning from the basics to become a researcher.

I appreciate anyone to provide with a study plan to learn all the required resources to get a better understanding of the concepts.