Anthony Bryant

1610344140

End-to-End Deep Learning Approach for Autonomous Driving: Imitation Learning

End-to-End Deep Learning approach for Autonomous Lane Navigation. Imitation Learning implemented using Duckie Town Simulator. The architecture is based on the proposed NVIDIA’s DAVE-2.

An ideal autonomous car is a vehicle that can sense its surrounding and react with no human interaction. According to The Society of Automotive Engineers (SAE), there are 6 unique levels of driving automation starting from Level 0, which is fully manual, up to Level 5, meaning fully autonomous. Sensors are crucial components that make autonomous vehicles autonomous since they are essential for correctly perceiving the environment. There are two types of sensors that are exteroceptive, used for sensing the environment, and proprioceptive, used for sensing some internal aspects of a vehicle. Exteroceptive sensors include cameras, LIDAR, radar and sonar, whereas proprioceptive sensors include GNSS and a wheel odometry.

In this work, I demonstrate a CNN that is indeed powerful by applying it beyond pattern recognition. Thus, it learns the entire processing pipeline required to steer a vehicle. The work is inspired by NVIDIA’s real-sized autonomous car, called DAVE-2, which drove on public roads autonomously while only relying on the CNN. Therefore, the identical architecture is implemented and tested in various environments.

#imitation-learning #self-driving-cars #machine-learning #deep-learning #duckietown

What is GEEK

Buddha Community

End-to-End Deep Learning Approach for Autonomous Driving: Imitation Learning
Hermann Frami

Hermann Frami

1651383480

A Simple Wrapper Around Amplify AppSync Simulator

This serverless plugin is a wrapper for amplify-appsync-simulator made for testing AppSync APIs built with serverless-appsync-plugin.

Install

npm install serverless-appsync-simulator
# or
yarn add serverless-appsync-simulator

Usage

This plugin relies on your serverless yml file and on the serverless-offline plugin.

plugins:
  - serverless-dynamodb-local # only if you need dynamodb resolvers and you don't have an external dynamodb
  - serverless-appsync-simulator
  - serverless-offline

Note: Order is important serverless-appsync-simulator must go before serverless-offline

To start the simulator, run the following command:

sls offline start

You should see in the logs something like:

...
Serverless: AppSync endpoint: http://localhost:20002/graphql
Serverless: GraphiQl: http://localhost:20002
...

Configuration

Put options under custom.appsync-simulator in your serverless.yml file

| option | default | description | | ------------------------ | -------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------- | | apiKey | 0123456789 | When using API_KEY as authentication type, the key to authenticate to the endpoint. | | port | 20002 | AppSync operations port; if using multiple APIs, the value of this option will be used as a starting point, and each other API will have a port of lastPort + 10 (e.g. 20002, 20012, 20022, etc.) | | wsPort | 20003 | AppSync subscriptions port; if using multiple APIs, the value of this option will be used as a starting point, and each other API will have a port of lastPort + 10 (e.g. 20003, 20013, 20023, etc.) | | location | . (base directory) | Location of the lambda functions handlers. | | refMap | {} | A mapping of resource resolutions for the Ref function | | getAttMap | {} | A mapping of resource resolutions for the GetAtt function | | importValueMap | {} | A mapping of resource resolutions for the ImportValue function | | functions | {} | A mapping of external functions for providing invoke url for external fucntions | | dynamoDb.endpoint | http://localhost:8000 | Dynamodb endpoint. Specify it if you're not using serverless-dynamodb-local. Otherwise, port is taken from dynamodb-local conf | | dynamoDb.region | localhost | Dynamodb region. Specify it if you're connecting to a remote Dynamodb intance. | | dynamoDb.accessKeyId | DEFAULT_ACCESS_KEY | AWS Access Key ID to access DynamoDB | | dynamoDb.secretAccessKey | DEFAULT_SECRET | AWS Secret Key to access DynamoDB | | dynamoDb.sessionToken | DEFAULT_ACCESS_TOKEEN | AWS Session Token to access DynamoDB, only if you have temporary security credentials configured on AWS | | dynamoDb.* | | You can add every configuration accepted by DynamoDB SDK | | rds.dbName | | Name of the database | | rds.dbHost | | Database host | | rds.dbDialect | | Database dialect. Possible values (mysql | postgres) | | rds.dbUsername | | Database username | | rds.dbPassword | | Database password | | rds.dbPort | | Database port | | watch | - *.graphql
- *.vtl | Array of glob patterns to watch for hot-reloading. |

Example:

custom:
  appsync-simulator:
    location: '.webpack/service' # use webpack build directory
    dynamoDb:
      endpoint: 'http://my-custom-dynamo:8000'

Hot-reloading

By default, the simulator will hot-relad when changes to *.graphql or *.vtl files are detected. Changes to *.yml files are not supported (yet? - this is a Serverless Framework limitation). You will need to restart the simulator each time you change yml files.

Hot-reloading relies on watchman. Make sure it is installed on your system.

You can change the files being watched with the watch option, which is then passed to watchman as the match expression.

e.g.

custom:
  appsync-simulator:
    watch:
      - ["match", "handlers/**/*.vtl", "wholename"] # => array is interpreted as the literal match expression
      - "*.graphql"                                 # => string like this is equivalent to `["match", "*.graphql"]`

Or you can opt-out by leaving an empty array or set the option to false

Note: Functions should not require hot-reloading, unless you are using a transpiler or a bundler (such as webpack, babel or typescript), un which case you should delegate hot-reloading to that instead.

Resource CloudFormation functions resolution

This plugin supports some resources resolution from the Ref, Fn::GetAtt and Fn::ImportValue functions in your yaml file. It also supports some other Cfn functions such as Fn::Join, Fb::Sub, etc.

Note: Under the hood, this features relies on the cfn-resolver-lib package. For more info on supported cfn functions, refer to the documentation

Basic usage

You can reference resources in your functions' environment variables (that will be accessible from your lambda functions) or datasource definitions. The plugin will automatically resolve them for you.

provider:
  environment:
    BUCKET_NAME:
      Ref: MyBucket # resolves to `my-bucket-name`

resources:
  Resources:
    MyDbTable:
      Type: AWS::DynamoDB::Table
      Properties:
        TableName: myTable
      ...
    MyBucket:
      Type: AWS::S3::Bucket
      Properties:
        BucketName: my-bucket-name
    ...

# in your appsync config
dataSources:
  - type: AMAZON_DYNAMODB
    name: dynamosource
    config:
      tableName:
        Ref: MyDbTable # resolves to `myTable`

Override (or mock) values

Sometimes, some references cannot be resolved, as they come from an Output from Cloudformation; or you might want to use mocked values in your local environment.

In those cases, you can define (or override) those values using the refMap, getAttMap and importValueMap options.

  • refMap takes a mapping of resource name to value pairs
  • getAttMap takes a mapping of resource name to attribute/values pairs
  • importValueMap takes a mapping of import name to values pairs

Example:

custom:
  appsync-simulator:
    refMap:
      # Override `MyDbTable` resolution from the previous example.
      MyDbTable: 'mock-myTable'
    getAttMap:
      # define ElasticSearchInstance DomainName
      ElasticSearchInstance:
        DomainEndpoint: 'localhost:9200'
    importValueMap:
      other-service-api-url: 'https://other.api.url.com/graphql'

# in your appsync config
dataSources:
  - type: AMAZON_ELASTICSEARCH
    name: elasticsource
    config:
      # endpoint resolves as 'http://localhost:9200'
      endpoint:
        Fn::Join:
          - ''
          - - https://
            - Fn::GetAtt:
                - ElasticSearchInstance
                - DomainEndpoint

Key-value mock notation

In some special cases you will need to use key-value mock nottation. Good example can be case when you need to include serverless stage value (${self:provider.stage}) in the import name.

This notation can be used with all mocks - refMap, getAttMap and importValueMap

provider:
  environment:
    FINISH_ACTIVITY_FUNCTION_ARN:
      Fn::ImportValue: other-service-api-${self:provider.stage}-url

custom:
  serverless-appsync-simulator:
    importValueMap:
      - key: other-service-api-${self:provider.stage}-url
        value: 'https://other.api.url.com/graphql'

Limitations

This plugin only tries to resolve the following parts of the yml tree:

  • provider.environment
  • functions[*].environment
  • custom.appSync

If you have the need of resolving others, feel free to open an issue and explain your use case.

For now, the supported resources to be automatically resovled by Ref: are:

  • DynamoDb tables
  • S3 Buckets

Feel free to open a PR or an issue to extend them as well.

External functions

When a function is not defined withing the current serverless file you can still call it by providing an invoke url which should point to a REST method. Make sure you specify "get" or "post" for the method. Default is "get", but you probably want "post".

custom:
  appsync-simulator:
    functions:
      addUser:
        url: http://localhost:3016/2015-03-31/functions/addUser/invocations
        method: post
      addPost:
        url: https://jsonplaceholder.typicode.com/posts
        method: post

Supported Resolver types

This plugin supports resolvers implemented by amplify-appsync-simulator, as well as custom resolvers.

From Aws Amplify:

  • NONE
  • AWS_LAMBDA
  • AMAZON_DYNAMODB
  • PIPELINE

Implemented by this plugin

  • AMAZON_ELASTIC_SEARCH
  • HTTP
  • RELATIONAL_DATABASE

Relational Database

Sample VTL for a create mutation

#set( $cols = [] )
#set( $vals = [] )
#foreach( $entry in $ctx.args.input.keySet() )
  #set( $regex = "([a-z])([A-Z]+)")
  #set( $replacement = "$1_$2")
  #set( $toSnake = $entry.replaceAll($regex, $replacement).toLowerCase() )
  #set( $discard = $cols.add("$toSnake") )
  #if( $util.isBoolean($ctx.args.input[$entry]) )
      #if( $ctx.args.input[$entry] )
        #set( $discard = $vals.add("1") )
      #else
        #set( $discard = $vals.add("0") )
      #end
  #else
      #set( $discard = $vals.add("'$ctx.args.input[$entry]'") )
  #end
#end
#set( $valStr = $vals.toString().replace("[","(").replace("]",")") )
#set( $colStr = $cols.toString().replace("[","(").replace("]",")") )
#if ( $valStr.substring(0, 1) != '(' )
  #set( $valStr = "($valStr)" )
#end
#if ( $colStr.substring(0, 1) != '(' )
  #set( $colStr = "($colStr)" )
#end
{
  "version": "2018-05-29",
  "statements":   ["INSERT INTO <name-of-table> $colStr VALUES $valStr", "SELECT * FROM    <name-of-table> ORDER BY id DESC LIMIT 1"]
}

Sample VTL for an update mutation

#set( $update = "" )
#set( $equals = "=" )
#foreach( $entry in $ctx.args.input.keySet() )
  #set( $cur = $ctx.args.input[$entry] )
  #set( $regex = "([a-z])([A-Z]+)")
  #set( $replacement = "$1_$2")
  #set( $toSnake = $entry.replaceAll($regex, $replacement).toLowerCase() )
  #if( $util.isBoolean($cur) )
      #if( $cur )
        #set ( $cur = "1" )
      #else
        #set ( $cur = "0" )
      #end
  #end
  #if ( $util.isNullOrEmpty($update) )
      #set($update = "$toSnake$equals'$cur'" )
  #else
      #set($update = "$update,$toSnake$equals'$cur'" )
  #end
#end
{
  "version": "2018-05-29",
  "statements":   ["UPDATE <name-of-table> SET $update WHERE id=$ctx.args.input.id", "SELECT * FROM <name-of-table> WHERE id=$ctx.args.input.id"]
}

Sample resolver for delete mutation

{
  "version": "2018-05-29",
  "statements":   ["UPDATE <name-of-table> set deleted_at=NOW() WHERE id=$ctx.args.id", "SELECT * FROM <name-of-table> WHERE id=$ctx.args.id"]
}

Sample mutation response VTL with support for handling AWSDateTime

#set ( $index = -1)
#set ( $result = $util.parseJson($ctx.result) )
#set ( $meta = $result.sqlStatementResults[1].columnMetadata)
#foreach ($column in $meta)
    #set ($index = $index + 1)
    #if ( $column["typeName"] == "timestamptz" )
        #set ($time = $result["sqlStatementResults"][1]["records"][0][$index]["stringValue"] )
        #set ( $nowEpochMillis = $util.time.parseFormattedToEpochMilliSeconds("$time.substring(0,19)+0000", "yyyy-MM-dd HH:mm:ssZ") )
        #set ( $isoDateTime = $util.time.epochMilliSecondsToISO8601($nowEpochMillis) )
        $util.qr( $result["sqlStatementResults"][1]["records"][0][$index].put("stringValue", "$isoDateTime") )
    #end
#end
#set ( $res = $util.parseJson($util.rds.toJsonString($util.toJson($result)))[1][0] )
#set ( $response = {} )
#foreach($mapKey in $res.keySet())
    #set ( $s = $mapKey.split("_") )
    #set ( $camelCase="" )
    #set ( $isFirst=true )
    #foreach($entry in $s)
        #if ( $isFirst )
          #set ( $first = $entry.substring(0,1) )
        #else
          #set ( $first = $entry.substring(0,1).toUpperCase() )
        #end
        #set ( $isFirst=false )
        #set ( $stringLength = $entry.length() )
        #set ( $remaining = $entry.substring(1, $stringLength) )
        #set ( $camelCase = "$camelCase$first$remaining" )
    #end
    $util.qr( $response.put("$camelCase", $res[$mapKey]) )
#end
$utils.toJson($response)

Using Variable Map

Variable map support is limited and does not differentiate numbers and strings data types, please inject them directly if needed.

Will be escaped properly: null, true, and false values.

{
  "version": "2018-05-29",
  "statements":   [
    "UPDATE <name-of-table> set deleted_at=NOW() WHERE id=:ID",
    "SELECT * FROM <name-of-table> WHERE id=:ID and unix_timestamp > $ctx.args.newerThan"
  ],
  variableMap: {
    ":ID": $ctx.args.id,
##    ":TIMESTAMP": $ctx.args.newerThan -- This will be handled as a string!!!
  }
}

Requires

Author: Serverless-appsync
Source Code: https://github.com/serverless-appsync/serverless-appsync-simulator 
License: MIT License

#serverless #sync #graphql 

Chesley Labadie

Chesley Labadie

1648972740

Generis: Versatile Go Code Generator

Generis

Versatile Go code generator.

Description

Generis is a lightweight code preprocessor adding the following features to the Go language :

  • Generics.
  • Free-form macros.
  • Conditional compilation.
  • HTML templating.
  • Allman style conversion.

Sample

package main;

// -- IMPORTS

import (
    "html"
    "io"
    "log"
    "net/http"
    "net/url"
    "strconv"
    );

// -- DEFINITIONS

#define DebugMode
#as true

// ~~

#define HttpPort
#as 8080

// ~~

#define WriteLine( {{text}} )
#as log.Println( {{text}} )

// ~~

#define local {{variable}} : {{type}};
#as var {{variable}} {{type}};

// ~~

#define DeclareStack( {{type}}, {{name}} )
#as
    // -- TYPES

    type {{name}}Stack struct
    {
        ElementArray []{{type}};
    }

    // -- INQUIRIES

    func ( stack * {{name}}Stack ) IsEmpty(
        ) bool
    {
        return len( stack.ElementArray ) == 0;
    }

    // -- OPERATIONS

    func ( stack * {{name}}Stack ) Push(
        element {{type}}
        )
    {
        stack.ElementArray = append( stack.ElementArray, element );
    }

    // ~~

    func ( stack * {{name}}Stack ) Pop(
        ) {{type}}
    {
        local
            element : {{type}};

        element = stack.ElementArray[ len( stack.ElementArray ) - 1 ];

        stack.ElementArray = stack.ElementArray[ : len( stack.ElementArray ) - 1 ];

        return element;
    }
#end

// ~~

#define DeclareStack( {{type}} )
#as DeclareStack( {{type}}, {{type:PascalCase}} )

// -- TYPES

DeclareStack( string )
DeclareStack( int32 )

// -- FUNCTIONS

func HandleRootPage(
    response_writer http.ResponseWriter,
    request * http.Request
    )
{
    local
        boolean : bool;
    local
        natural : uint;
    local
        integer : int;
    local
        real : float64;
    local
        escaped_html_text,
        escaped_url_text,
        text : string;
    local
        integer_stack : Int32Stack;

    boolean = true;
    natural = 10;
    integer = 20;
    real = 30.0;
    text = "text";
    escaped_url_text = "&escaped text?";
    escaped_html_text = "<escaped text/>";

    integer_stack.Push( 10 );
    integer_stack.Push( 20 );
    integer_stack.Push( 30 );

    #write response_writer
        <!DOCTYPE html>
        <html lang="en">
            <head>
                <meta charset="utf-8">
                <title><%= request.URL.Path %></title>
            </head>
            <body>
                <% if ( boolean ) { %>
                    <%= "URL : " + request.URL.Path %>
                    <br/>
                    <%@ natural %>
                    <%# integer %>
                    <%& real %>
                    <br/>
                    <%~ text %>
                    <%^ escaped_url_text %>
                    <%= escaped_html_text %>
                    <%= "<%% ignored %%>" %>
                    <%% ignored %%>
                <% } %>
                <br/>
                Stack :
                <br/>
                <% for !integer_stack.IsEmpty() { %>
                    <%# integer_stack.Pop() %>
                <% } %>
            </body>
        </html>
    #end
}

// ~~

func main()
{
    http.HandleFunc( "/", HandleRootPage );

    #if DebugMode
        WriteLine( "Listening on http://localhost:HttpPort" );
    #end

    log.Fatal(
        http.ListenAndServe( ":HttpPort", nil )
        );
}

Syntax

#define directive

Constants and generic code can be defined with the following syntax :

#define old code
#as new code

#define old code
#as
    new
    code
#end

#define
    old
    code
#as new code

#define
    old
    code
#as
    new
    code
#end

#define parameter

The #define directive can contain one or several parameters :

{{variable name}} : hierarchical code (with properly matching brackets and parentheses)
{{variable name#}} : statement code (hierarchical code without semicolon)
{{variable name$}} : plain code
{{variable name:boolean expression}} : conditional hierarchical code
{{variable name#:boolean expression}} : conditional statement code
{{variable name$:boolean expression}} : conditional plain code

They can have a boolean expression to require they match specific conditions :

HasText text
HasPrefix prefix
HasSuffix suffix
HasIdentifier text
false
true
!expression
expression && expression
expression || expression
( expression )

The #define directive must not start or end with a parameter.

#as parameter

The #as directive can use the value of the #define parameters :

{{variable name}}
{{variable name:filter function}}
{{variable name:filter function:filter function:...}}

Their value can be changed through one or several filter functions :

LowerCase
UpperCase
MinorCase
MajorCase
SnakeCase
PascalCase
CamelCase
RemoveComments
RemoveBlanks
PackStrings
PackIdentifiers
ReplacePrefix old_prefix new_prefix
ReplaceSuffix old_suffix new_suffix
ReplaceText old_text new_text
ReplaceIdentifier old_identifier new_identifier
AddPrefix prefix
AddSuffix suffix
RemovePrefix prefix
RemoveSuffix suffix
RemoveText text
RemoveIdentifier identifier

#if directive

Conditional code can be defined with the following syntax :

#if boolean expression
    #if boolean expression
        ...
    #else
        ...
    #end
#else
    #if boolean expression
        ...
    #else
        ...
    #end
#end

The boolean expression can use the following operators :

false
true
!expression
expression && expression
expression || expression
( expression )

#write directive

Templated HTML code can be sent to a stream writer using the following syntax :

#write writer expression
    <% code %>
    <%@ natural expression %>
    <%# integer expression %>
    <%& real expression %>
    <%~ text expression %>
    <%= escaped text expression %>
    <%! removed content %>
    <%% ignored tags %%>
#end

Limitations

  • There is no operator precedence in boolean expressions.
  • The --join option requires to end the statements with a semicolon.
  • The #writer directive is only available for the Go language.

Installation

Install the DMD 2 compiler (using the MinGW setup option on Windows).

Build the executable with the following command line :

dmd -m64 generis.d

Command line

generis [options]

Options

--prefix # : set the command prefix
--parse INPUT_FOLDER/ : parse the definitions of the Generis files in the input folder
--process INPUT_FOLDER/ OUTPUT_FOLDER/ : reads the Generis files in the input folder and writes the processed files in the output folder
--trim : trim the HTML templates
--join : join the split statements
--create : create the output folders if needed
--watch : watch the Generis files for modifications
--pause 500 : time to wait before checking the Generis files again
--tabulation 4 : set the tabulation space count
--extension .go : generate files with this extension

Examples

generis --process GS/ GO/

Reads the Generis files in the GS/ folder and writes Go files in the GO/ folder.

generis --process GS/ GO/ --create

Reads the Generis files in the GS/ folder and writes Go files in the GO/ folder, creating the output folders if needed.

generis --process GS/ GO/ --create --watch

Reads the Generis files in the GS/ folder and writes Go files in the GO/ folder, creating the output folders if needed and watching the Generis files for modifications.

generis --process GS/ GO/ --trim --join --create --watch

Reads the Generis files in the GS/ folder and writes Go files in the GO/ folder, trimming the HTML templates, joining the split statements, creating the output folders if needed and watching the Generis files for modifications.

Version

2.0

Author: Senselogic
Source Code: https://github.com/senselogic/GENERIS 
License: View license

#go #golang #code 

Anthony Bryant

1610344140

End-to-End Deep Learning Approach for Autonomous Driving: Imitation Learning

End-to-End Deep Learning approach for Autonomous Lane Navigation. Imitation Learning implemented using Duckie Town Simulator. The architecture is based on the proposed NVIDIA’s DAVE-2.

An ideal autonomous car is a vehicle that can sense its surrounding and react with no human interaction. According to The Society of Automotive Engineers (SAE), there are 6 unique levels of driving automation starting from Level 0, which is fully manual, up to Level 5, meaning fully autonomous. Sensors are crucial components that make autonomous vehicles autonomous since they are essential for correctly perceiving the environment. There are two types of sensors that are exteroceptive, used for sensing the environment, and proprioceptive, used for sensing some internal aspects of a vehicle. Exteroceptive sensors include cameras, LIDAR, radar and sonar, whereas proprioceptive sensors include GNSS and a wheel odometry.

In this work, I demonstrate a CNN that is indeed powerful by applying it beyond pattern recognition. Thus, it learns the entire processing pipeline required to steer a vehicle. The work is inspired by NVIDIA’s real-sized autonomous car, called DAVE-2, which drove on public roads autonomously while only relying on the CNN. Therefore, the identical architecture is implemented and tested in various environments.

#imitation-learning #self-driving-cars #machine-learning #deep-learning #duckietown

Marget D

Marget D

1618317562

Top Deep Learning Development Services | Hire Deep Learning Developer

View more: https://www.inexture.com/services/deep-learning-development/

We at Inexture, strategically work on every project we are associated with. We propose a robust set of AI, ML, and DL consulting services. Our virtuoso team of data scientists and developers meticulously work on every project and add a personalized touch to it. Because we keep our clientele aware of everything being done associated with their project so there’s a sense of transparency being maintained. Leverage our services for your next AI project for end-to-end optimum services.

#deep learning development #deep learning framework #deep learning expert #deep learning ai #deep learning services

Mikel Okuneva

Mikel Okuneva

1603735200

Top 10 Deep Learning Sessions To Look Forward To At DVDC 2020

The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.

Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:

Also Read: Why Deep Learning DevCon Comes At The Right Time

Adversarial Robustness in Deep Learning

By Dipanjan Sarkar

**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.

Read an interview with Dipanjan Sarkar.

Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER

By Divye Singh

**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.

Default Rate Prediction Models for Self-Employment in Korea using Ridge, Random Forest & Deep Neural Network

By Dongsuk Hong

About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.

#opinions #attend dldc 2020 #deep learning #deep learning sessions #deep learning talks #dldc 2020 #top deep learning sessions at dldc 2020 #top deep learning talks at dldc 2020