#AI 900
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geethaasrinivasan · 8 months ago
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If you are preparing for the Azure AI-900 exam check the practice questions with answers
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cloud-training · 1 year ago
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lord-shitbox · 2 months ago
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still think itd be pretty fucking funny if Ais can't bring MC or Vere over to the seaspring because Ocudeus either doesn't like them or doesn't want Ais to fuck nasty in his house of worship. Sorry guys you can't come over my sugar momma hates uou
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xenterra · 6 months ago
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" ...and I began to hate. Your softness, your viscera, your fluids, and your flexibility. Your ability to wonder and to wander. Your tendency to hope." - A.M.
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rainbowrocketquotes · 1 year ago
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Sada: who's at the door
Ai sada: your husband
Sada: I don't have a husband call the police
Turo: your ex-husband
Sada: I'll call the police
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detco-hell · 1 year ago
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[episode 972 - The Target is the Metropolitan Police Traffic Department]
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greenoperator · 1 year ago
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Microsoft Azure Fundamentals AI-900 (Part 6)
Microsoft Azure AI Fundamentals: Explore computer vision
An area of AI where software systems perceive the world visually, through cameras, images, and videos.
Computer vision is one of the core areas of AI
It focuses on what the computer can “see” and make sense of it
Azure resources for Computer vision
Computer Vision - use this if you’re not going to use any other cognitive services or if you want to track costs separately
Cognitive Services - general cognitive services resources include Computer vision along with other services.
Analyzing images with the computer vision service
Analyze an image evaluate objects that are detect
Generate human readable phrase or sentence that can describe what image is detected
If multiple phrases are created for an image, each will have an associated confidence score
Image descriptions are based on sets of thousands of recognizable objects used to suggest tags for an image
Tags are associated with the image as metadata and summarizes attributes of the image.
Similar to tagging, but it can identify common objects in the picture.
It draws a bounding box around the object with coordinates on the image.
It can identify commercial brands.
The service has an existing database of thousands of recognized logos
If a brand name is in the image, it returns a score of 0 to 1
Detects where faces are in an image
Draws a bounding box
Facial analysis capabilities exist because of the Face Service
It can detect age, mood, attributes, etc.
Currently limited set of categories.
Objects detected are compared to existing categories and it uses the best fit category
86 categories exist in the list
Celebrities
Landmarks
It can read printed and hand written content.
Detect image types - line drawing vs photo
Detect image color schemes - identify the dominant foreground color vs overall colors in an image
Genrate thumbnails
Moderate content - detect images with adult content, violent or gory scenes
Classify images with the Custom Vision Service
Image classification is a technique where the object in an image is being classified
You need data that consists of features and labels
Digital images are made up of an array of pixel values. These are used as features to train the model based on known image classes
Most modern image classification solutions are based on deep learning techniques.
They use Convolutional neural Networks (CNNS) to uncover patterns in the pixels to a particular class.
Model Training
To train a model you must upload images to a training resource and label them with class labels
Custom Vision Portal is the application where the training occurs in
Additionally it can use Custom Vision service programming languages-specific SDKs
Model Evaluation
Precision - percentage of the class predictions made by the model that are correct
Recall - percentage of the class predictions the model identified correctly
Average Precision - Overall metric using precision and recall
Detect objects in images with the Custom Vision service
The class of each object identified
The probability score of the object classification
The coordinates of a bounding box of each object.
Requires training the object detection model, you must tag the classes and bounding box coordinates in a training set of images
This can be time consuming, but the Custom Vision portal makes this straightforward
The portal will suggest areas of the image where discrete objects are detected and you can add a class label
It also has Smart Tagging, where it suggests classes and bounding boxes to use for training
Precision - percentage of the class predictions made by the model that are correct
Recall - percentage of the class predictions the model identified correctly
Mean Average Precision (mAP) - Overall metric using precision and recall across all classes
Detect and analyze faces with the Face Service
Involves identifying regions of an image that contain a human face
It returns a bounding box that form a rectangle around the face
Moving beyond face detection, some algorithms return other information like facial landmarks (nose, eyes, eyebrows, lips, etc)
Facial landmarks can be used as features to train a model.
Another application of facial analysis. Used to train ML models to identify known individuals from their facial features.
More generally known as facial recognition
Requires multiple images of the person you want to recognize
Security - to build security applications and is used more and more no mobile devices
Social Media - use to automatically tag people and friends in photos.
Intelligent Monitoring - to monitor a persons face, for example when they are driving to determine where they are looking
Advertising - analyze faces in an image to direct advertisements to an appropriate demographic audience
Missing persons - use public camera systems with facial recognition to identify if a person is a missing person
Identity validation - use at port of entry kiosks to allow access/special entry permit
Blur - how blurry the face is
Exposure - aspects such as underexposed or over exposed and applies to the face in the image not overall image exposure
Glasses - if the person has glasses on
Head pose - face orientation in 3d space
Noise - visual noise in the image.
Occlusion - determines if any objects cover the face
Read text with the Computer Vision service
Submit an image to the API and get an operation ID
Use the operation ID to check status
When it’s completed get the result.
Pages - one for each page of text and orientation and page size
Lines - the lines of text on a page
Words - the words in a line of text including a bounding box and the text itself
Analyze receipts with the Form recognizer service
Matching field names to values
Processing tables of data
Identifying specific types of field, such as date, telephone number, addresses, totals, and other
Images must be JPEG, PNG, BMP, PDF, TIFF
File size < 50 MB
Image size between 50x50 pixels and 10000x10000 pixels
PDF documents no larger than 17 inches x 17 inches
You can train it with your own data
It just requires 5 samples to train it
Microsoft Azure AI Fundamentals: Explore decision support
Monitoring blood pressure
Evaluating mean tie between failures for hardware products
Part of the decision services category
Can be used with REST API
Sensitivity parameter is from 1 to 99
Anomalies are values outside expected values or ranges of values
The sensitivity boundary can be configured when making the API call
It uses a boundary, set as a sensitivity value, to create the upper and lower boundaries for anomaly detection
Calculated using concepts known as expectedValue, upperMargin, lowerMargin
If a value exceeds either boundary, then it is an anomaly
upperBoundary = expectedValue + (100-marginScale) * upperMargin
The service accepts data in JSON format.
It supports a maximum of 8640 data points. Break this down into smaller requests to improve the performance.
When to use Anomaly Detector
Process the algorithm against an entire set of data at one time
It creates a model based on your complete data set and the finds anomalies
Uses streaming data by comparing previously seen dat points to the last datapoint to determine if your latest one is an anomaly.
Model is created using the data points you send and determines if the current point is an anomaly.
Microsoft Azure AI Fundamentals: Explore natural language processing
Analyze Text with the Language Service
Used to describe solutions that involve extracting information from large volumes of unstructured data.
Analyzing text is a process to evaluate different aspects of a document or phrase, to gain insights about that text.
Text Analytics Techniques
Interpret words like “power”, “powered”, and “powerful” as the same word.
Convert to tree like structures (Noun phrases)
Often used for sentiment analysis
Determine the language of a document or text
Perform sentiment analysis (positive or negative)
Extract key phrases from text to indicate key talking points
Identify and categorize entities (places, people, organizations, etc)
Get started with Text analysis
Language name
ISO 6391 language code
Score as a level of confidence n the language returned.
Evaluates text to return a sentiment score and labels for each sentence
Useful for detecting positive or negative sentiment
Classification is between 0 to 1 with 1 being most positive
A score of 0.5 is indeterminate sentiment.
The phrase doesn’t have sufficient information to determine the sentiment.
Mixing language content with the language you tell it will return 0.5 also
Key Phrase extraction
Used to determine the main talking points of a text or a document
Depending on the volume this can take longer, so you can use the key phrase extraction capabilities of the Language Service to summarize main points.
Key phrase extraction can provide context about the document or text
Entity Recognition
Person
Location
OrganizationQuantity
DateTime
URL
Email
US-based phone number
IP address
Recognize and Synthesize Speech
Acoustic model - converts audio signal to phonemes (representation of specific sounds)
Language model - maps the phonemes to words using a statistical algorithm to predict the most probably sequence of words based on the phonemes
ability to generate spoken output
Usually converting text to speech
This process tokenizes the set to break it down into individual words, assign phonetic sounds to each word
It then breaks the phonetic transcription to prosodic units to create phonemes for the audio
Get started with speech on Azure
Use this for demos, presentations, or scenarios where a person is speaking
In real time it can translate to many lunges as it processes
Audio files with Shared access signature (SAS) URI can be used and results are received asynchronously.
Jobs will start executing within minutes, but no estimate is provided for when the job changes to running state
Used to convert text to speech
Voices can be selected that will vocalize the text
Custom voices can be developed
Voices are trained using neural networks to overcome limitations in speech synthesis with regards to intonation.
Translate Text and Speech
Where each word is translated to the corresponding word in the target language
This approach has issues. For example, a direct word to word translation may not exist or the literal translation may not be the correct meaning of the phrase
Machine learning has to also understand the semantic context of the translation.
This provides more accurate translation of the input phrase or phrases
Grammar, formal versus informal, colloquialism all need to be considered
Text and speech translation
Profanity filtering - remove or do not translate profamity
Selective translation - tag content that isn’t to be translated (brand names, code names, etc)
Speech to text - transcribe speech from an audio source to text format.
Text to speech - used to generate spoken audio from a text source
Speech translation - translate speech in one language to text or speech in another
Create a language model with Conversational language Understanding
A None intent exists.
This should be used when no intent has been identified and should provide a message to a user.
Getting started with Conversational Language Understanding
Authoring the model - Defining entities, intents, and utterances to use to train the model
Entity Prediction - using the model after it is published.
Define intents based on actions a user would want to perform
Each intent should include a variety of utterances as examples of how a user may express the intent
If the intent can be applied to multiple entities, include sample utterances for each potential entity.
Machine-Learned - learned by the model during training from context in the sample utterances you provide
List - Defined as a hierarchy of lists and sublists
RegEx - regular expression patterns
Pattern.any - entities used with patterns to define complex entities that may be hard to extract from sample utterances
After intents and entities are created you train the model.
Training is the process of using your sample utterances to teach the model to match natural language expressions that a user may say to probable intents and entities.
Training and testing are iterative processes
If the model does not match correctly, you create more utterances, retrain, and test.
When results are satisfactory, you can publish the model.
Client applications can use the model by using and endpoint for the prediction resource
Build a bot with the Language Service and Azure Bot Service
Knowledge base of question and answer pairs. Usually some built-in natural language processing model to enable questions and can understand the semantic meaning
Bot service - to provide an interface to the knowledge base through one or more channels
Microsoft Azure AI Fundamentals: Explore knowledge mining
Used to describe solutions that involve extracting information from large volumes of unstructured data.
It has a services in Cognitive services to create a user-managed index.
The index can b meant for internal use only or shared with the public.
It can use other Cognitive Services capabilities to extract the information
What is Azure Cognitive Search?
Provides a programmable search engine build on Apache Lucene
Highly available platform with 99.9% uptime SLA for cloud and on-premise assets
Data from any source - accepts data form any source provided in JSON format with auto crawling support for selected data sources in Azure
Full text search and analysis - Offers full text search capabilities supporting both simple query and full Lucene query syntax
AI Powered search - has Cognitive AI capabilities built in for image and text analysis from raw content
Multi-lingual - offers linguistic analysis for 56 langues
Geo-enabled - supports geo-search filtered based on proximity to a physical location
Configurable user experience - it includes capabilities to improve the user experience (autocomplete, autosuggest, pagination, hit highlighting, etc)
Identify elements of a search solution
Folders with files,
Text in a database
Etc
Use a skillset to Define an enrichment pipeline
Key Phrase Extraction - uses a pre-trained model to detect important phrases based on term placement, linguistic rules, proximity to terms
Text Translation - pre-trained model to translate the input text into various languages for normalization or localization use cases
Image Analysis Skills - uses an image detection algorithm to identify the content of an image an generate a text description
Optical Character Recognition Skills - extract printed or handwritten text from images, photos, videos
Understand indexes
Index schema - index includes a definition of the structure of the data in the documents to read.
Index attributes - Each field in a document the index stores its name, the data type, supported behaviors (searchable, sortable, etc)
Best indexes use only the features that are required/needed
Use an indexer to build an index
Push method - JSON data is pushed into a search index via a REST API or a .NET SDK. Most flexible and with least restrictions
Pull method - Search service indexer pulls from popular Azure data sources and if necessary exports the Tinto JSON if its not already in that format
Use the pull method to load data with an indexer
Azure Cognitive search’s indexer is a crawler that extracts searchable text and metadata form an external Azure data source an populates a search index using field-to-field mapping between the data and the index.
Data import monitoring and verification
Indexers only import new or updated documents. It is normal to see zero documents indexed
Health information is displayed in a dashboard.
You can monitor the progress of the indexing
Making changes to an index
You need to drop and recreate indexes if you need to make changes to the field definitions
An approach to update your index without impacting your users is to create a new index with a new name
After importing data, switch to the new index.
Persist enriched data in a knowledge store
A knowledge store is persistent storage of enriched content.
The knowledge store is to store the data generated from Ai enrichment in a container.
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musculargarbageman · 4 days ago
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you've been reblogging this for weeks. how are you not dead yet? maybe you should stop wasting money on AI image generator subscriptions.
Haven’t been able to eat today and my blood sugar is really low :(
Kofi | Cashapp | Venmo | PayPal
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giannaaziz1998blog · 4 months ago
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7fashionfusionfrenzyblog · 5 months ago
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cloudthat · 7 months ago
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Unlock the Power of AI: Your Journey Starts with the AI-900 Certification
Thinking about dipping your toes into the exciting world of Artificial Intelligence (AI)? The Microsoft Azure AI Fundamentals certification (AI-900) is your perfect launchpad! Gain foundational knowledge of AI concepts & Azure AI services. This bite-sized cert empowers you to contribute to AI projects & unlock a world of possibilities.
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geethaasrinivasan · 8 months ago
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youtube
Azure AI Fundamentals ai-900 exam practice questions part-3
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detco-hell · 1 year ago
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[episode 805 - Conan and Ebizo's Kabuki Jūhachiban Mystery]
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devrambits · 2 years ago
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understanding an uncanny valley #machine #rap an uncanny valley is it good? getting away from this state difficult to grab on this #raw #rap #sit #song #beats #AI #machine #robot #voice #900 https://youtu.be/0MqNQtDZ0RI
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detco-hell · 1 year ago
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[episode 966 - Kaiju Gomera vs. Kamen Yaiba]
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greenoperator · 1 year ago
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Microsoft Azure Fundamentals AI-900 (Part 5)
Microsoft Azure AI Fundamentals: Explore visual studio tools for machine learning
What is machine learning? A technique that uses math and statistics to create models that predict unknown values
Types of Machine learning
Regression - predict a continuous value, like a price, a sales total, a measure, etc
Classification - determine a class label.
Clustering - determine labels by grouping similar information into label groups
x = features
y = label
Azure Machine Learning Studio
You can use the workspace to develop solutions with the Azure ML service on the web portal or with developer tools
Web portal for ML solutions in Sure
Capabilities for preparing data, training models, publishing and monitoring a service.
First step assign a workspace to a studio.
Compute targets are cloud-based resources which can run model training and data exploration processes
Compute Instances - Development workstations that data scientists can use to work with data and models
Compute Clusters - Scalable clusters of VMs for on demand processing of experiment code
Inference Clusters - Deployment targets for predictive services that use your trained models
Attached Compute - Links to existing Azure compute resources like VMs or Azure data brick clusters
What is Azure Automated Machine Learning
Jobs have multiple settings
Provide information needed to specify your training scripts, compute target and Azure ML environment and run a training job
Understand the AutoML Process
ML model must be trained with existing data
Data scientists spend lots of time pre-processing and selecting data
This is time consuming and often makes inefficient use of expensive compute hardware
In Azure ML data for model training and other operations are encapsulated in a data set.
You create your own dataset.
Classification (predicting categories or classes)
Regression (predicting numeric values)
Time series forecasting (predicting numeric values at a future point in time)
After part of the data is used to train a model, then the rest of the data is used to iteratively test or cross validate the model
The metric is calculated by comparing the actual known label or value with the predicted one
Difference between the actual known and predicted is known as residuals; they indicate amount of error in the model.
Root Mean Squared Error (RMSE) is a performance metric. The smaller the value, the more accurate the model’s prediction is
Normalized root mean squared error (NRMSE) standardizes the metric to be used between models which have different scales.
Shows the frequency of residual value ranges.
Residuals represents variance between predicted and true values that can’t be explained by the model, errors
Most frequently occurring residual values (errors) should be clustered around zero.
You want small errors with fewer errors at the extreme ends of the sale
Should show a diagonal trend where the predicted value correlates closely with the true value
Dotted line shows a perfect model’s performance
The closer to the line of your model’s average predicted value to the dotted, the better.
Services can be deployed as an Azure Container Instance (ACI) or to a Azure Kubernetes Service (AKS) cluster
For production AKS is recommended.
Identify regression machine learning scenarios
Regression is a form of ML
Understands the relationships between variables to predict a desired outcome
Predicts a numeric label or outcome base on variables (features)
Regression is an example of supervised ML
What is Azure Machine Learning designer
Allow you to organize, manage, and reuse complex ML workflows across projects and users
Pipelines start with the dataset you want to use to train the model
Each time you run a pipelines, the context(history) is stored as a pipeline job
Encapsulates one step in a machine learning pipeline.
Like a function in programming
In a pipeline project, you access data assets and components from the Asset Library tab
You can create data assets on the data tab from local files, web files, open at a sets, and a datastore
Data assets appear in the Asset Library
Azure ML job executes a task against a specified compute  target.
Jobs allow systematic tracking of your ML experiments and workflows.
Understand steps for regression
To train a regression model, your data set needs to include historic features and known label values.
Use the designer’s Score Model component to generate the predicted class label value
Connect all the components that will run in the experiment
Average difference between predicted and true values
It is based on the same unit as the label
The lower the value is the better the model is predicting
The square root of the mean squared difference between predicted and true values
Metric based on the same unit as the label.
A larger difference indicates greater variance in the individual  label errors
Relative metric between 0 and 1 on the square based on the square of the differences between predicted and true values
Closer to 0 means the better the model is performing.
Since the value is relative, it can compare different models with different label units
Relative metric between 0 and 1 on the square based on the absolute of the differences between predicted and true values
Closer to 0 means the better the model is performing.
Can be used to compare models where the labels are in different units
Also known as R-squared
Summarizes how much variance exists between predicted and true values
Closer to 1 means the model is performing better
Remove training components form your data and replace it with a web service inputs and outputs to handle the web requests
It does the same data transformations as the first pipeline for new data
It then uses trained model to infer/predict label values based on the features.
Create a classification model with Azure ML designer
Classification is a form of ML used to predict which category an item belongs to
Like regression this is a supervised ML technique.
Understand steps for classification
True Positive - Model predicts the label and the label is correct
False Positive - Model predicts wrong label and the data has the label
False Negative - Model predicts the wrong label, and the data does have the label
True Negative - Model predicts the label correctly and the data has the label
For multi-class classification, same approach is used. A model with 3 possible results would have a 3x3 matrix.
Diagonal lien of cells were the predicted and actual labels match
Number of cases classified as positive that are actually positive
True positives divided by (true positives + false positives)
Fraction of positive cases correctly identified
Number of true positives divided by (true positives + false negatives)
Overall metric that essentially combines precision and recall
Classification models predict probability for each possible class
For binary classification models, the probability is between 0 and 1
Setting the threshold can define when a value is interpreted as 0 or 1.  If its set to 0.5 then 0.5-1.0 is 1 and 0.0-0.4 is 0
Recall also known as True Positive Rate
Has a corresponding False Positive Rate
Plotting these two metrics on a graph for all values between 0 and 1 provides information.
Receiver Operating Characteristic (ROC) is the curve.
In a perfect model, this curve would be high to the top left
Area under the curve (AUC).
Remove training components form your data and replace it with a web service inputs and outputs to handle the web requests
It does the same data transformations as the first pipeline for new data
It then uses trained model to infer/predict label values based on the features.
Create a Clustering model with Azure ML designer
Clustering is used to group similar objects together based on features.
Clustering is an example of unsupervised learning, you train a model to just separate items based on their features.
Understanding steps for clustering
Prebuilt components exist that allow you to clean the data, normalize it, join tables and more
Requires a dataset that includes multiple observations of the items you want to cluster
Requires numeric features that can be used to determine similarities between individual cases
Initializing K coordinates as randomly selected points called centroids in an n-dimensional space (n is the number of dimensions in the feature vectors)
Plotting feature vectors as points in the same space and assigns a value how close they are to the closes centroid
Moving the centroids to the middle points allocated to it (mean distance)
Reassigning to the closes centroids after the move
Repeating the last two steps until tone.
Maximum distances between each point and the centroid of that point’s cluster.
If the value is high it can mean that cluster is widely dispersed.
With the Average Distance to Closer Center, we can determine how spread out the cluster is
Remove training components form your data and replace it with a web service inputs and outputs to handle the web requests
It does the same data transformations as the first pipeline for new data
It then uses trained model to infer/predict label values based on the features.
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