AIPM
részletek a world doksiban
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részletek a world doksiban
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IMPLEMENTATION: https://fullstackdeeplearning.com/march2019
Elmélet: http://d2l.ai/chapter_optimization/adagrad.html
Overview: https://www.youtube.com/watch?time_continue=62&v=Rb2GChTYR5Y&feature=emb_logo
https://www.youtube.com/watch?time_continue=78&v=5eaza1YTGv4&feature=emb_logo
Breaking down the Problem
https://www.youtube.com/watch?time_continue=265&v=0ew7NYl7z4Y&feature=emb_logo
METRICS!
https://www.youtube.com/watch?time_continue=30&v=D3AMs0HtYWs&feature=emb_logo
Need AI? (Value of AI/ML)
https://www.youtube.com/watch?time_continue=81&v=OHG7GBIye3c&feature=emb_logo
https://www.youtube.com/watch?time_continue=185&v=MUHaV7gzRsE&feature=emb_logo
SUMMARY
https://www.youtube.com/watch?time_continue=155&v=fLPz48A7TSc&feature=emb_logo
TEAM
https://www.youtube.com/watch?time_continue=27&v=6xqxzhPkysw&feature=emb_logo
https://www.youtube.com/watch?time_continue=120&v=z7A44YnJqCw&feature=emb_logo
Data size: enough data?
Precision & Recall
Precision and recall are just different metrics for measuring the "success" or performance of a trained model.
precision is defined as the number of true positives (truly fraudulent transaction data, in this case) over all positives, and will be the higher when the amount of false positives is low.
recall is defined as the number of true positives over true positives plus false negatives and will be higher when the number of false negatives is low.
Both take into account true positives and will be higher for high, positive accuracy, too.
I find it helpful to look at the below image to wrap my head around these measurements:
Data Annotation
If you look at different datasets used for training machine learning models; they often come in a tabular format—a file that contains a bunch of information about different data points (often a .csv spreadsheet). An example showing both the distribution—how many data points fall into which column ranges—and the different features, such as petal length and width, of different species of Iris, is shown below.
Adding Annotations via a Platform
To annotate a new data source that perhaps only includes images of flowers and no other identifying labels or features, you'll have to a data annotation platform. These platforms will send unlabeled data to some human annotators who can classify or provide features for the data and send it back to you in a tabular format. Some cloud service providers like AWS provide data annotation services as do specific companies; data annotation tooling is what the company, Figure Eight does and so we will use their platform as an example, but the skills you learn here about designing labels and creating a dataset will be applicable, across different platforms.
Figure Eight's Platform
The best way to learn about Figure Eight's data annotation tools is to explore the platform homepage. Here, you will see examples of use cases for labeling text, speech, image data, and more!
The goal of data annotation is to bring you from unstructured, unlabeled data, to a desired, labeled output. Figure Eight will send your data to human annotators that can help transform unlabeled data.
From unstructured to structured data
DATA ANNOTATION
You should design a data annotation job, such that a non-expert can identify more noticeable cases of pneumonia. Since you are designing for a non-expert annotator, you should design for failure; this means including some way to capture uncertainty in your data labels and test questions.
Project Proposal
<your answer text here>
<your text here>
Say you’ve run a test launch and gotten back results from your annotators; the instructions and test questions are rated below 3.5, what areas of your Instruction document would you try to improve (Examples, Test Questions, etc.)
<your text here>
How to Use: Column Mapper: https://success.figure-eight.com/hc/en-us/articles/209638343-How-to-Use-Column-Mapper
How to Format Data for Uploading: https://success.figure-eight.com/hc/en-us/articles/202703175-How-to-Format-Data-for-Uploading
How to add Data: https://success.figure-eight.com/hc/en-us/articles/201855629-How-to-Add-Data
folytatni ezzel: https://success.figure-eight.com/hc/en-us/articles/209975853-Design-Graphical-Editor
Training Data
https://www.youtube.com/watch?time_continue=14&v=-YNBV1NAZO0&feature=emb_logo
Model Evaluation
https://www.youtube.com/watch?time_continue=312&v=q1BZhEixJW4&feature=emb_logo
Transfer Learning
https://www.youtube.com/watch?time_continue=189&v=TN35rUd9wUc&feature=emb_logo
Model Building
Measuring Business Impact
példa:
https://www.youtube.com/watch?time_continue=27&v=Xzdm0FhwYMY&feature=emb_logo
A/B testing
https://www.youtube.com/watch?time_continue=133&v=2jCgvJLLWyM&feature=emb_logo
Unwanted bias
https://www.youtube.com/watch?time_continue=129&v=dYP5YpZlkTQ&feature=emb_logo
Active learning: https://www.youtube.com/watch?time_continue=11&v=vGb42P2biEs&feature=emb_logo
Model Optimazition: https://www.youtube.com/watch?time_continue=103&v=mKz7u8TilEo&feature=emb_logo
CASE STUDY (Figure eight)
1., Identify the Business Problem: https://www.youtube.com/watch?time_continue=116&v=vXFfcntI8mQ&feature=emb_logo
2., Prototyping
https://www.youtube.com/watch?time_continue=77&v=LUk-6erdj9o&feature=emb_logo
https://www.youtube.com/watch?time_continue=342&v=dJ3SXbrtnsc&feature=emb_logo
esettanulmányhoz
https://developer.ibm.com/patterns/enhance-customer-help-desk-with-smart-document-understanding/
https://developer.ibm.com/patterns/watson-studio-nlc-technical-support-ticket-categorization/
https://developer.ibm.com/patterns/virtual-helpdesk/
Folyatni
https://fullstackdeeplearning.com/march2019
machine learning project: 29perc
https://learning.oreilly.com/library/view/building-machine-learning/9781492045106/ch02.html
https://learning.oreilly.com/library/view/machine-learning-for/9781617295836/kindle_split_012.html
