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AI-900 > part 1 > Flashcards

part 1 Flashcards

(65 cards)

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1
Q

AI define

A

Machines performing jobs mimicking human behaviour

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2
Q

ML define

A

Foundation of an AI system, learns and predicts like a human
Machines that get better without explicit programming

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3
Q

DL define

A

Machines that have an artificial NN inspired by the human brain to solve complex problems

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4
Q

Data scientist define

A

Person with multi-disciplinary skills in maths, stats, predictive modelling and ML to make future predictions

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5
Q

Describe onion diagram of AI, ML, DL

A

AI contains ML which contains DL

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6
Q

Anomoly detection

A

Detects outliers or things out of place like a human

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7
Q

Computer vision

A

be able to see like a human

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8
Q

NLP

A

Be able to process human languages and infer context

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9
Q

Conversational AI

A

be able to hold a conversation with a human

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10
Q

What is a dataset

A

Logical grouping of units of data that are closely related and/or share the same data structure

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11
Q

MNIST

A

Images of handwritten digits used to test classification, clustering and image processing algorithms e.g. computer vision ML models

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12
Q

COCO (common objects in context) dataset

A

Contains common images using a JSON file (coco format) that identify objects or segments within an image
- features object segmentation, recognision in context, superpixel stuff segmentation
Azure has a data labelling service which can export in coco format

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13
Q

Data labeling

A

Identifying raw data and adding one more more meaningful and informative labels to provide context so ML model can learn

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14
Q

data labelling - supervised

A

Labels are a prerequisite to produce training data. Each piece generally labelled by a human

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15
Q

data labelling - unsupervised

A

Labels produced by machine, might not be human readable

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16
Q

ground truth

A

Properly labelled dataset used as objective standard to train and assess the model. Accuracy of trained model depends on accuracy of ground truth

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17
Q

Supervised learning

A

Data that has been labelled for training.
Task-driven - make a prediction
When the labels are known and you want a precise outcome. You need a specific value returned e.g. Classification, Regression

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18
Q

Unsupervised learning

A

Data has not been labelled, ML model needs to do its own labelling
Data-driven - recognise a structure or pattern
When labels not known and outcome doesn’t need to be precise.
Trying to make sense of data.
e.g. Clustering, dimensionality reduction, association

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19
Q

Reinforcement learning

A

No data, there is an environment and an ML model generates data any many times to reach a goal
Decisions-driven - Game AI, Learning Tasks, Robot Navigation

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20
Q

Neural network

A

Mimicking the brain. Node/neuron represents an algorithm
Data inputted into neuron and based on output, data passed to one of many other connected neurons.
Connections are weighted.
Network is organised into layers
Input layer, many hidden, and an output

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21
Q

How many layers for a NN to be called deep learning

A

3+

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22
Q

Feed Forward (FNN)

A

Neural networks where connections between nodes don’t form a cycle (always moving forward)

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23
Q

Back propagation

A

Moves backwards through the neural network adjusting weights to improve next iteration’s performance. How the Neural net learns.

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24
Q

Loss function

A

Function comparing ground truth to prediction to determine error rate. Performs calculation at the end, performs calculation and then back propagates.

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25
Activation functions
Algorithm applied to a hidden layer node that affects connected output (e.g. ReLu, part of backpropagation)
26
Dense
When the next layer increases the number of nodes
27
Sparse
When the next layer decreases the amount of nodes (dimensionality reduction is when nodes decrease from one layer to the next)
28
GPU
General processing unit specifically designed to render high res images and video concurrently Can perform parallel operations on multiple sets of data - used for non-graph tasks e.g. ML and scientific computation
29
CPU cores vs GPU cores
CPU - 4-16 processor cores on average GPU - thousands of processor cores
30
CUDA (compute unified device architecture)
Parallel computing platform and API by NVIDIA allowing developers to use CUDA-enabled GPUs for general purpose computing on GPUs (GPGPU)
31
NVIDIA
Company manufactures GPUs for gaming and professional markets
32
Major deep learning frameworks are integrated with
NVIDIA deep learning SDK - collection of NVIDIA libraries for deep learning, e.g. cuDNN (CUDA deep neural network library)
33
ML Pipeline stages
Data labelling -> supervised learning so model can learn by example Feature engineering -> translate data to format ML models can understand Training -> multiple iterations, getting smarter Hyperparameter tuning -> Try different parameters to optimise outcome Serving -> So model is accessible, host in VM or container Inference -> requesting to make prediction e.g. real time endpoint (for one request), or batch processing (slower, but could also be real time)
34
Forecasting
Future prediction with relevant data: analysis of trends, not 'guessing'
35
Prediction
Make future prediction without relevant data: uses statistics to predict future outcomes, more 'guessing', uses decision theory
36
Performance/Evaluation metrics
Used to evaluate ML algorithms:
37
Different types of metrics for different problems
- Classification metrics (accuracy, precision, recall, F1-score, ROC, AUC) - Regression metrics (MSE, RMSE, MAE) - Ranking metrics - Statistical metrics (correlation) - Computer vision metrics
38
Jupyter Lab
Will replace Jupyter notebook
39
Regression
Process of finding a function to correlate a labelled dataset (supervised) into continuous variable/number e.g. what will temperature be
40
Regression error
Distance of vector from regression line. Used to predict future variables - MSE, RMSE, MAE
41
Classification
Finding a function to divide a labelled dataset into classes/categories e.g. what weather category will it be. (supervised)
42
Classification line
Divides dataset with one side being one category, another being another category
43
Classification algorithms
Logistic regression, decision tree/random forest, neural networks, naive bayes, k nearest neighbours, SVM
44
Clustering
Process of grouping unlabelled data based on similarities/differences (unsupervised) e.g. K-means, K-medoids, Density based, Hierarchichal
45
Confusion Matrix
Visualise model predictions vs ground truth labels (actual). Aka error matrix. Top labels: predicted no, predicted yes Side labels: actual no, actual yes
46
Size of confusion matrix
Number of categories x 2 (ground truth x prediction)
47
use cases for anomaly detection
data cleaning intrusion detection + fraud detection systems health monitoring sensor networks event detection ecosystem disturbances ML is more accurate than by hand and more efficient + accurate
48
computer vision DL algorithms
CNN - image + video recognition inspired by how eyes process info + send to brain Recurrent NN (RNN) - handwriting/speech recognition
49
Types of computer vision
image classification object detection semantic segmentation (identify segments + objects by drawing pixel mask) - good for objects in movement image analysis - analyse image/video to apply descriptive + context labels optical character recognition facial detections
50
Azure Computer Vision. iOS app built
Seeing AI developed for iOS, use device camera to identify people + objects + device audibly describes for visually impaired
51
Azure computer vision service offering
'Computer Vision' - analyse image/videos + extract description, tags, objects, text 'Custom vision' - Custom image classification + object detection models using own images Face - Detect + identify people and emotions in images Form recogniser - translate scanned docs into key/val or tabular editable data
52
NLP
ML that understands context of a corpus enabling - analyse/interpret text in docs/emails - interpret + contextualise spoken token e.g. sentiment analysis - synthesise speech - automatically translate - interpret spoken or written commands + determine appropriate actions
53
NLP Azure service offering
Text analytics - sentiment analysis, key phrase extraction, identify language, entity recognition Translator - real-time translation Speech - transcribe into searchable text LUIS (Language understanding) - NLP enabling understanding human language in own application
54
Conversational AI
Tech that can participate in conversations w humans: chatbots, voice assistants, Interactive Voice Recognition Systems
55
Conversational AI use cases
Online Customer Supports Accessibility e.g. visually impaired HR Processes - employee training Healthcare IoT Software e.g. autocomplete search
56
Conversational AI Azure services
QnA Maker - create conversational q and a bot from knowledge base Azure Bot Service - deploys the bot created with QnA maker. Intelligent serverless bot service scaling on demand. For creating/publishing/managing bots.
57
Responsible AI
ethical, transparent + accountable use of AI
58
Microsoft AI principles
Fairness Reliability + Safety Privacy + Security Inclusiveness Transparency Accountability
59
Principle: Fairness
AI systems should treat all people fairly Bias can be introduced during pipeline development, reinforcing societal stereotypes E.g. systems dealing w opportunities/resources/info in criminal justice/employment/finance Azure ML can tell you how each feature can influence model's prediction for bias
60
Principle: Reliability + Safety
Should perform reliably + safely. Rigorous testing needed to ensure works as expected before end user release + shortcomings reported to user. Critical safety importance: Autonomous vehicle, AI health diagnosis, autonomous weapons
61
Principle: Privacy + Security
Nature of ML model may require personally identifiable information Ensure data protected so no leaking/disclosing. Some cases, model can be run on user's device avoiding vulnerability
62
Principle Privacy + Security: AI security principles to detect malicious actors
data origin and lineage, data use internal vs external, data corruption considerations, anomaly detection
63
Principle: Inclusiveness
Design AI solutions for minority then can design AI for majority e.g. physical ability, gender, sexual orientation, ethnicity etc
64
Principle: Transparency
AI systems should be understandable. Interpretability/intelligibility is when end users can understand UI behaviour. Transparency: mitigates unfairness, helps debug systems, gains user trust Open about why using AI+ limitations Open source AI framework can help
65
Principle: Accountability
Structure put in place enacting AI principles + putting into account. AI should work within framework of governance + organisational principles, ethical + legal standards clearly defined. Principles guide Microsoft on how they develop, sell + advocate when working w 3rd parties, pushing towards regulations towards AI principles

AI-900 (2 decks)

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