Learn Statistical Machine Learning Perspective of Deep Learning with examples. This is a Complete course of Statistical Machine Learning. You can easily read and download free this course. Theory of Statistical Machine learning distribute with the problem of finding a predictive function based on data. Theory of Statistical learning is a structure for machine learning drawing from the fields of statistics and practical analysis. Purpose of this course is to learn structure of Deep Generative Models. In this course you will also know about structure simplifies representation.
|Course name||Learn About Statistical Machine Learning Perspective of Deep Learning with real life examples|
Actually, Statistics is a tool that can be used to get answers to important questions about data. Descriptive statistical is a methods to transform underdone observations which convert into information that can be understandable and allowance. In this course you will learn about different topics of Statistical Machine Learning in detail and also understand it by with examples.
- You will learn basics of probabilistic graphical models in detail.
- Will be able to learn about neural network
- You will learn about units, layers, activations functions, loss functions and other building blocks.
- Also know about DL component of Reverse-mode automatic differentiation.
- Detail knowledge of graphical models and computational graphs.
- Learn about graphical models with potential functions represented by NNs.
- Read Bayesian learning with NNs parameters.
- In this course you will learn Kernel learning.
- Fundamental question and answers of probabilistic modeling.
- Differences of Graphical models and Deep nets.