Machine Learning - Statistical Analysis Flashcards
What is regression analysis?
A statistical process of estimating the relationships among variables to identify which variables have impact on a topic of interest.
The process of performing a regression allows you to confidently determine which factors matter most, which factors can be ignored, and how these factors influence each other.
It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or ‘predictors’).
More specifically, regression analysis helps one understand how the typical value of the dependent variable (or ‘criterion variable’) changes when any one of the independent variables is varied, while the other independent variables are held fixed.
What is Statistical classification?
In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. An example would be assigning a given email into “spam” or “non-spam” classes or assigning a diagnosis to a given patient as described by observed characteristics of the patient (gender, blood pressure, presence or absence of certain symptoms, etc.). Classification is an example of pattern recognition.
What is Tensor Flow?
An open-source library for machine learning and AI. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks.
- Machine learning library used by many Google products
- Open sources in 2015
- C++ engine and API
- Python client API that talks to C++
- Deep learning neural networks with auto-differentiation of objective functions
What is Neural Networking?
A machine learning method that uses interconnected nodes or neurons in a layered structure. These are used to process and learn from data in a way that is inspired by the human brain.
It creates an adaptive system that computers use to learn from their mistakes and improve continuously.
The network is asked to solve a problem, which it attempts to do over and over, each time strengthening the connections that lead to success and diminishing those that lead to failure.
Thus, artificial neural networks attempt to solve complicated problems, like summarizing documents or recognizing faces, with greater accuracy.
A neural network of more than three layers would be considered a deep learning algorithms while under three layers is a basic neural network.
