Microsoft AI Flashcards
Read API
The Read API uses the latest recognition models and is optimized for images that have a significant amount of text or has considerable visual noise.
The Read API is a better option for scanned documents that have a lot of text. The Read API also has the ability to automatically determine the proper recognition model to use, taking into consideration lines of text and supporting images with printed text as well as recognizing handwriting.
Read API
Pages - One for each page of text, including information about the page size and orientation.
Lines - The lines of text on a page.
Words - The words in a line of text.
NLP
NLP enables you to create software that can:
Analyze text documents to extract key phrases and recognize entities (such as places, dates, or people).
Perform sentiment analysis to determine how positive or negative the language used in a document is.
Interpret spoken language, and synthesize speech responses.
Automatically translate spoken or written phrases between languages.
Interpret commands and determine appropriate actions.
Composer
Open source solution, not a command line program
visual authoring tool
Data splitting
Data sets can be divided using regular expressions
Improving face detection
Increase shutter speed
AUC
area under the curve is used to evaluate classification models
Datasets for Azure automated Machine learning
Tabular, File
Azure Machine Learning Designer
Datasets cannot be connected directly to each other
Modules can be connected directly
Pipeline endpoints cannot be used to receive and send data real time
Regression Performance Metrics
Mean Absolute Error (MAE): The average difference between predicted values and true values. This value is based on the same units as the label, in this case dollars. The lower this value is, the better the model is predicting.
Root Mean Squared Error (RMSE): The square root of the mean squared difference between predicted and true values. The result is a metric based on the same unit as the label (dollars). When compared to the MAE (above), a larger difference indicates greater variance in the individual errors (for example, with some errors being very small, while others are large).
Relative Squared Error (RSE): A relative metric between 0 and 1 based on the square of the differences between predicted and true values. The closer to 0 this metric is, the better the model is performing. Because this metric is relative, it can be used to compare models where the labels are in different units.
Relative Absolute Error (RAE): A relative metric between 0 and 1 based on the absolute differences between predicted and true values. The closer to 0 this metric is, the better the model is performing. Like RSE, this metric can be used to compare models where the labels are in different units.
Coefficient of Determination (R2): This metric is more commonly referred to as R-Squared, and summarizes how much of the variance between predicted and true values is explained by the model. The closer to 1 this value is, the better the model is performing.
Q&A maker
authoring and query prediction
Application Insights
collects chatbot logs and telemetry
Cognitive Search
sores Q&A pairs and stores indexes
Bing Visual Search
will find webpages related to an image
Custom Vision
classification, object detection
