AAS 390 Chapter Notes - Chapter 1: Deutsche Forschungsgemeinschaft, Hyperparameter Optimization, Conference On Neural Information Processing Systems

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yingmingcy1

29 Jun 2022

School

A&M Texarkana, TAMUT

Department

Applied Arts & Sciences

Course

AAS 390

Professor

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Machine learning problems in bci: meinel1, k. eggensperger2, m. tangermann1*, f. hutter2. 1brain state decoding lab, 2machine learning for automated algorithm design group (both:) cluster of excellence brainlinks-braintools, dept. Introduction: pipelines for bci data analysis comprise several building blocks, such as signal preprocessing, feature extraction, decoding of features and output shaping for the bci application at hand. These components contain many hyperparameters, such as frequency bands, time intervals, regularization factors, adaptation parameters, etc. , which need to be chosen carefully in order to obtain optimal overall performance. As even simple bci setups comprise tens of mutually dependent (discrete or continuous) hyperparameters, the search space is too large for a full grid search. Even though experts can tune most parameters based on experience, the inter-subject variability inherent to bcis is likely to reward a subject-dependent optimization strategy.

Related Questions

8. Write a class called WordSet representing a set of String objects which can contain an arbitrary number of strings, limited only by the memory available to the Java Virtual Machine. A set is a collection of elements, however, unlike a list, a set cannot contain duplicate elements. Therefore, as its name implies, a WordSet object cannot contain duplicate Strings that is, if one attempts to da String to a Wordset, and the wordset already contains an element which is equal to this string, the attempt will fail and the state of the Wordset will not change. All String comparisons performed in the methods defined in the Wordset class are done in a Case SENSITIVE manner. Note that in the method descriptions below, the phrase "this Wordset" means "the Wordset on which the method in question is called." Your Wordset class MUST provide the following INSTANCE methods • A constructor, which takes no parameters and initializes the new Wordset ohject so that it contains no elements • A method called add(), which takes as its only parameter a string and returns a value of type boolean. If this Wordset already contains a string which is equal to the parameter string, or the parameter String is null, then the state of this Wordset does not change and the method return false; otherwise, the parameter String is added to this Wordset. and the method returns true. • A method called remove().which takes as its only parameter a string and returns a value of type boolean. If this wordSet contains a string which is equal to the parameter String this string is removed from the WordSet and the method returns true. On the other hand, if the string parameter is null or this Wordset does not contain a string which is equal to the parameter string, then the state of this wordset does not change, and the method return false. • A method called get.size(), which takes no parameters and returns the number of strings currently stored in this Wordset. This method MUST NOT change the state of this Wordset. • A method called isMember(). which takes as its only parameter a string and retumsa value of type boolean which is true if this wordset contains the parameter String false otherwise. If the parameter String is null, this method also return false. This method MUST NOT change the state of this wordset. • A method called computebirrerence().which takes as its only parameter a Wordset and returns a new Wordset representing the difference between this wordset and the parameter Wordset: in other words, the WordSet returned by this method contains all the strings that are in this wordset, but not in the parameter Wordset. If the parameter Wordset contains all the elements of this WordSet or this WordSet contains no elements, then this method returns a new Wordset containing no elements. Finally, if the parameter Wordset is null or contains ne strings, then this method returns a new wordset which contains all the elements of this wordset. This method MUST NOT change the state of this wordset or the parameter Wordset. • A method called tostring , which takes the parameters and turns a string which is the textual representation of this wordset. This textual representation consists of the concatena tion of the following clements - The String - The text representation of each clement in the WordSet; cach pair of adjacent clements is separated by the String", but this String MUST NOT appear before the first clement or after the last element - The String") The order in which the elements of this Wordset appear in the textual representation generated by the tostring() method does not matter. If the WordSet contains to Brings, then the textual representation returned by this method is ". Note that you MUST respect proper encapsulation practices that is, the attributes and methods of your class MUST be declared using the proper visibility modifier NINT Um an array list (of string) to keep track of the string stored in a wordt ob joot Is there a method defined in the Array loist class which determines whether in clementis already wored in an array ist, and which will do a case-sensitive comparison when liked on an Arraylint of strings? See the pages 24-26 of this examination for information about potentially useful methods defined by the ArrayList class WRITE YOUR Wordset CLASS IN THE SPACE BELOW:

In this assignment, we build a simple ANN model for a dataset and we explore some methods to optimize model parameters.

Dependencies

Python > 2.7
Scikit Learn package
Pandas toolkit (If it is needed) Numpy toolkit (If it is needed)

Dataset

This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.

Note: You do not need to download the data. Two subsets of data for training and test is created and posted for downloading. The original data set also is included for your further testing and experimenting.

The list of the fields in order of columns in the data file is:
PregnanciesNumber of times pregnant
GlucosePlasma glucose concentration 2 hours in an oral glucose tolerance test BloodPressureDiastolic blood pressure (mm Hg)
SkinThicknessTriceps skin fold thickness (mm)
Insulin2-Hour serum insulin (mu U/ml)
BMIBody mass index (weight in kg/(height in m)^2)
DiabetesPedigreeFunctionDiabetes pedigree function
Age (years)
OutcomeClass variable (0 No Diabetes or 1 Diabetes) 268 of 768 are 1, the others are 0.

ANNprojectdescription
– Loading the required libraries and modules
To finish this assignment, you may use any module that you think is necessary. These modules includes:
1- Pandas
2- Numpy
3- Matplotlib
4- Sklearn

– Reading the data and performing basic data checks

1- Use describe() method of the dataframe (e.g df.describe()) to output the data dimensions and fields basic stats.

– Creating the train and test datasets

1- Splityourdataintotrain,testand,validation.
2- Splityourdatabasedontheratiooftrain=0.75,validation=0.15and,test=0.10
3- Printoutthetotalnumberofrowsandthenumberofrowsallocatedforeach

category above.
Note1: There are many ways you can achieve splitting dataset.
You can use any method as long as you have a balanced ratio of classes in each category.
4- Useyourtestdatasetonlyforfinaltesting.Youshouldnotusetestdatasetfor adjusting parameters.

– Building your model

1- Useneural_networkclassifiertobuildyournetwork.
2- Thereareafewparametersneedstobeadjustedinthismodel:

a. Hidden_layer_sizes b. Activation
c. Solver
d. Batch_size

e. Learning_rate
f. Learning_rate_init
3- Mostofparametersabovehavedefaultvalues.Youmayrunyournetworkon default values to get an idea about your network
4- Youmayadjustparametersbasedwhatwetalkedintheclassetc

– Network metric and plots

1- To evaluate your network, calculate confusion matrix. For that you may use confusion_matrix function from sklearn.metrics library.
How do you interpret precision and recall?
2- Plot error plot for each iteration (epoch) for both validate and train.
Based on your parameter setting you may see an overfitting problem. Try to spot it if that’s happened.
3- Once you finish your parameter tunning, run your model on your test data set and create a plot for your test the same as previous step

Whatareyourdeliverables?

a. A Jupyter document containing your model and experiment results.

dha2021

Dear Hardikmaths3,

the last few questions I post, you keep saying 1. Can you please confirm the following questions?

You are building a chatbot for a restaurant by using the Bot Framework Composer.

Users must be able to browse available meal combinations and select the one they want.

You need to ensure that the users can use the chatbot to browse images, prices, and descriptions of the food and select a meal combination.

What feature should you include?

Select only one answer.

1. TextCard

2. carousel

3. SuggestedAction

4. cardReceipt card

Answer:

You plan to use Text Analytics to detect the language of reviews.

You need to route the reviews that are multi-lingual to a human translator.

Which field in the JSON response should you use to filter records?

Select only one answer.

1. score

2. errors

3. statistics

4. name

Answer:

You have an application that performs Speech to Text services for users by using the Speech SDK.

You need to add the ability to perform Text to Speech in the application.

Besides the text, which other values should you include in your queries?

Select only one answer.

1. the pronunciation

2. the voice

3. a keyword

$. the phrase list

the answer:

You build an application that uses Language Understanding.

You need to ensure that the application support speech inputs by using the Speech service.

What should you configure for Language Understanding?

Select only one answer.

1. Enable the Speech priming setting during model publishing.

2. Add additional utterances to the intents in the model before publishing.

3. Set the endpoint URL parameter verbose to true.

4. Enable the Normalize punctuation setting on the application.

mmyint1234

AAS 390 Chapter Notes - Chapter 1: Deutsche Forschungsgemeinschaft, Hyperparameter Optimization, Conference On Neural Information Processing Systems

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