
%%html
<style type='text/css'>
.CodeMirror {
    font-size: 14px; 
    font-family: 'Jetbrains Mono';
}
</style>


from IPython.display import display, HTML
display(HTML("<style>.container { width:85% !important; }</style>"))


import pandas as pd

pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
pd.set_option('display.max_colwidth', 1000)
pd.set_option("display.max_rows", None, "display.max_columns", None)


import seaborn as sns
import matplotlib.pyplot as plt


from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, StringType, IntegerType, DoubleType, BooleanType
from pyspark.sql.functions import *


spark = (SparkSession
        .builder
        .appName("meta-kaggle-data-analysis")
        .getOrCreate()
        )

23/08/16 07:22:23 WARN SparkSession: Using an existing Spark session; only runtime SQL configurations will take effect.


spark


data_files_path = "/Volumes/samsung-2tb/rk/downloads/archive"


!ls -lhS $data_files_path

total 54139600
-rw-rw-r--@ 1 rk  staff    12G 11 Aug 11:20 EpisodeAgents.csv
-rw-rw-r--@ 1 rk  staff   4.0G 11 Aug 11:43 UserAchievements.csv
-rw-rw-r--@ 1 rk  staff   3.3G 11 Aug 11:36 Episodes.csv
-rw-rw-r--@ 1 rk  staff   1.5G 11 Aug 11:40 Submissions.csv
-rw-rw-r--@ 1 rk  staff   1.3G 11 Aug 11:39 KernelVersions.csv
-rw-rw-r--@ 1 rk  staff   796M 11 Aug 11:45 Users.csv
-rw-rw-r--@ 1 rk  staff   748M 11 Aug 11:18 DatasetVersions.csv
-rw-rw-r--@ 1 rk  staff   663M 11 Aug 11:38 ForumMessages.csv
-rw-rw-r--@ 1 rk  staff   505M 11 Aug 11:42 Teams.csv
-rw-rw-r--@ 1 rk  staff   279M 11 Aug 11:42 TeamMemberships.csv
-rw-rw-r--@ 1 rk  staff   198M 11 Aug 11:39 KernelVersionDatasetSources.csv
-rw-rw-r--@ 1 rk  staff   156M 11 Aug 11:40 KernelVotes.csv
-rw-rw-r--@ 1 rk  staff   144M 11 Aug 11:40 Kernels.csv
-rw-rw-r--@ 1 rk  staff   130M 11 Aug 11:38 ForumMessageVotes.csv
-rw-rw-r--@ 1 rk  staff    64M 11 Aug 11:19 DatasetVotes.csv
-rw-rw-r--@ 1 rk  staff    51M 11 Aug 11:45 UserFollowers.csv
-rw-rw-r--@ 1 rk  staff    46M 11 Aug 11:39 ForumTopics.csv
-rw-rw-r--@ 1 rk  staff    39M 11 Aug 11:39 KernelVersionCompetitionSources.csv
-rw-rw-r--@ 1 rk  staff    29M 11 Aug 11:19 Datasets.csv
-rw-rw-r--@ 1 rk  staff    21M 11 Aug 11:39 KernelTags.csv
-rw-rw-r--@ 1 rk  staff    19M 11 Aug 11:39 KernelVersionKernelSources.csv
-rw-rw-r--@ 1 rk  staff    13M 11 Aug 11:19 Datasources.csv
-rw-rw-r--@ 1 rk  staff    11M 11 Aug 11:39 Forums.csv
-rw-rw-r--@ 1 rk  staff   7.6M 11 Aug 11:18 DatasetTasks.csv
-rw-rw-r--@ 1 rk  staff   6.5M 11 Aug 11:17 DatasetTags.csv
-rw-rw-r--@ 1 rk  staff   2.5M 11 Aug 11:17 Competitions.csv
-rw-rw-r--@ 1 rk  staff   654K 11 Aug 11:17 DatasetTaskSubmissions.csv
-rw-rw-r--@ 1 rk  staff   260K 11 Aug 11:40 Organizations.csv
-rw-rw-r--@ 1 rk  staff    93K 11 Aug 11:42 Tags.csv
-rw-rw-r--@ 1 rk  staff    62K 11 Aug 11:45 UserOrganizations.csv
-rw-rw-r--@ 1 rk  staff    19K 11 Aug 11:17 CompetitionTags.csv
-rw-rw-r--@ 1 rk  staff   410B 11 Aug 11:39 KernelLanguages.csv


competitions_file = f"{data_files_path}/Competitions.csv"


competitions_schema = StructType([
    StructField("Id",IntegerType()),
    StructField("Slug",StringType()),
    StructField("Title",StringType()),
    StructField("Subtitle",StringType()),
    StructField("HostSegmentTitle",StringType()),
    StructField("ForumId",IntegerType()),
    StructField("OrganizationId",IntegerType()),
    StructField("EnabledDate",StringType()),
    StructField("DeadlineDate",StringType()),
    StructField("ProhibitNewEntrantsDeadlineDate",StringType()),
    StructField("TeamMergerDeadlineDate",StringType()),
    StructField("TeamModelDeadlineDate",StringType()),
    StructField("ModelSubmissionDeadlineDate",StringType()),
    StructField("FinalLeaderboardHasBeenVerified",BooleanType()),
    StructField("HasKernels",BooleanType()),
    StructField("OnlyAllowKernelSubmissions",BooleanType()),
    StructField("HasLeaderboard",BooleanType()),
    StructField("LeaderboardPercentage",IntegerType()),
    StructField("LeaderboardDisplayFormat",StringType()),
    StructField("EvaluationAlgorithmAbbreviation",StringType()),
    StructField("EvaluationAlgorithmName",StringType()),
    StructField("EvaluationAlgorithmDescription",StringType()),
    StructField("EvaluationAlgorithmIsMax",BooleanType()),
    StructField("MaxDailySubmissions",IntegerType()),
    StructField("NumScoredSubmissions",IntegerType()),
    StructField("MaxTeamSize",IntegerType()),
    StructField("BanTeamMergers",BooleanType()),
    StructField("EnableTeamModels",BooleanType()),
    StructField("RewardType",StringType()),
    StructField("RewardQuantity",IntegerType()),
    StructField("NumPrizes",IntegerType()),
    StructField("UserRankMultiplier",IntegerType()),
    StructField("CanQualifyTiers",BooleanType()),
    StructField("TotalTeams",IntegerType()),
    StructField("TotalCompetitors",IntegerType()),
    StructField("TotalSubmissions",IntegerType()),
    StructField("ValidationSetName",StringType()),
    StructField("ValidationSetValue",StringType()),
    StructField("EnableSubmissionModelHashes",BooleanType()),
    StructField("EnableSubmissionModelAttachments",BooleanType()),
    StructField("HostName",StringType()),
    StructField("CompetitionTypeId",IntegerType()),
])


competitions = spark.read.csv(competitions_file, header=True, schema=competitions_schema)
competitions.createOrReplaceTempView("competitions")

competitions.printSchema()

root
 |-- Id: integer (nullable = true)
 |-- Slug: string (nullable = true)
 |-- Title: string (nullable = true)
 |-- Subtitle: string (nullable = true)
 |-- HostSegmentTitle: string (nullable = true)
 |-- ForumId: integer (nullable = true)
 |-- OrganizationId: integer (nullable = true)
 |-- EnabledDate: string (nullable = true)
 |-- DeadlineDate: string (nullable = true)
 |-- ProhibitNewEntrantsDeadlineDate: string (nullable = true)
 |-- TeamMergerDeadlineDate: string (nullable = true)
 |-- TeamModelDeadlineDate: string (nullable = true)
 |-- ModelSubmissionDeadlineDate: string (nullable = true)
 |-- FinalLeaderboardHasBeenVerified: boolean (nullable = true)
 |-- HasKernels: boolean (nullable = true)
 |-- OnlyAllowKernelSubmissions: boolean (nullable = true)
 |-- HasLeaderboard: boolean (nullable = true)
 |-- LeaderboardPercentage: integer (nullable = true)
 |-- LeaderboardDisplayFormat: string (nullable = true)
 |-- EvaluationAlgorithmAbbreviation: string (nullable = true)
 |-- EvaluationAlgorithmName: string (nullable = true)
 |-- EvaluationAlgorithmDescription: string (nullable = true)
 |-- EvaluationAlgorithmIsMax: boolean (nullable = true)
 |-- MaxDailySubmissions: integer (nullable = true)
 |-- NumScoredSubmissions: integer (nullable = true)
 |-- MaxTeamSize: integer (nullable = true)
 |-- BanTeamMergers: boolean (nullable = true)
 |-- EnableTeamModels: boolean (nullable = true)
 |-- RewardType: string (nullable = true)
 |-- RewardQuantity: integer (nullable = true)
 |-- NumPrizes: integer (nullable = true)
 |-- UserRankMultiplier: integer (nullable = true)
 |-- CanQualifyTiers: boolean (nullable = true)
 |-- TotalTeams: integer (nullable = true)
 |-- TotalCompetitors: integer (nullable = true)
 |-- TotalSubmissions: integer (nullable = true)
 |-- ValidationSetName: string (nullable = true)
 |-- ValidationSetValue: string (nullable = true)
 |-- EnableSubmissionModelHashes: boolean (nullable = true)
 |-- EnableSubmissionModelAttachments: boolean (nullable = true)
 |-- HostName: string (nullable = true)
 |-- CompetitionTypeId: integer (nullable = true)

23/08/16 07:22:41 WARN package: Truncated the string representation of a plan since it was too large. This behavior can be adjusted by setting 'spark.sql.debug.maxToStringFields'.


competitions.limit(1).toPandas()


spark.sql("SELECT COUNT(*) FROM competitions").toPandas()


competition_summary = spark.sql("""
WITH temp AS (
SELECT
    Slug
  , Title
  , Subtitle
  , EvaluationAlgorithmName
  , EvaluationAlgorithmDescription
  , CASE
      WHEN LENGTH(EnabledDate) >= 10 AND 
           regexp_extract(EnabledDate, r"(\d{2}/\d{2}/\d{4})", 1) != "" AND
           regexp_extract(EnabledDate, r"(\d{2}/\d{2}/\d{4})", 1) IS NOT NULL THEN
          to_date(SUBSTR(EnabledDate, 1, 10), "MM/dd/yyyy")
      ELSE
          '2010-01-01'
    END AS EnabledDate
FROM
    competitions
)
SELECT
    EnabledDate
  , YEAR(EnabledDate) AS competition_year
  , Slug
  , Title
  , Subtitle
  , EvaluationAlgorithmName
  , EvaluationAlgorithmDescription    
FROM
    temp
""")

competition_summary.createOrReplaceTempView("competition_summary")
spark.sql("SELECT * FROM competition_summary ORDER BY EnabledDate DESC LIMIT 20").toPandas()


df = spark.sql("""
SELECT
    competition_year
  , COUNT(*) AS count
FROM
    competition_summary
GROUP BY
    competition_year
ORDER BY
    competition_year
""").toPandas()


# Create a bar plot using Seaborn
plt.figure(figsize=(10, 6))
sns.barplot(x='competition_year', y='count', data=df)
plt.title('Yearly Count')
plt.xlabel('Year')
plt.ylabel('Count')
plt.show()


spark.sql("""
SELECT
    Id
  , Title
  , MaxDailySubmissions
  , NumScoredSubmissions
  , MaxTeamSize
  , BanTeamMergers
  , EnableTeamModels
  , RewardType
  , NVL(RewardQuantity, 0) AS RewardQuantity
  , NumPrizes
  , UserRankMultiplier
  , CanQualifyTiers
  , TotalTeams
  , TotalCompetitors
  , TotalSubmissions
FROM
    competitions
LIMIT 5
""").toPandas()


spark.sql("""
SELECT
    Id
  , Title
  , Slug
  , Subtitle
  , MaxDailySubmissions
  , NumScoredSubmissions
  , MaxTeamSize
  , BanTeamMergers
  , EnableTeamModels
  , RewardType
  , NVL(RewardQuantity, 0) AS RewardQuantity
  , NumPrizes
  , UserRankMultiplier
  , CanQualifyTiers
  , TotalTeams
  , TotalCompetitors
  , TotalSubmissions
FROM
    competitions
ORDER BY
    TotalCompetitors DESC
LIMIT 5
""").toPandas()


spark.sql("""
SELECT
    *
FROM
    competitions 
ORDER BY TotalSubmissions DESC
LIMIT 3
""").toPandas()


spark.sql("""
SELECT
    Title
  , Subtitle
FROM
    competitions
ORDER BY
    Title
""").toPandas()


spark.stop()

	EnabledDate	competition_year	Slug	Title	Subtitle	EvaluationAlgorithmName	EvaluationAlgorithmDescription
0	2023-07-11	2023	playground-series-s3e19	Forecasting Mini-Course Sales	Playground Series - Season 3, Episode 19	SMAPE	Symmetric Mean Absolute Percentage Error
1	2023-06-27	2023	playground-series-s3e18	Explore Multi-Label Classification with an Enzyme Substrate Dataset	Playground Series - Season 3, Episode 18	Mean Columnwise Area Under Receiver Operating Characteristic Curve	Average of the per-column AUC-ROC scores.
2	2023-06-13	2023	playground-series-s3e17	Binary Classification of Machine Failures	Playground Series - Season 3, Episode 17	Area Under Receiver Operating Characteristic Curve	Measures discrimination. Calculates how well an algorithm separates true positives from false positives. Overall good metric for classification problems. Has range [0.5, 1.0]
3	2023-05-30	2023	playground-series-s3e16	Regression with a Crab Age Dataset	Playground Series - Season 3, Episode 16	Mean Absolute Error	Average of the absolute value of each individual error.
4	2023-05-22	2023	hubmap-hacking-the-human-vasculature	HuBMAP - Hacking the Human Vasculature	Segment instances of microvascular structures from healthy human kidney tissue slides.	OpenImagesObjDetectionSegmentationAP	OpenImagesObjDetectionSegmentationAP
5	2023-05-16	2023	playground-series-s3e15	Feature Imputation with a Heat Flux Dataset	Playground Series - Season 3, Episode 15	Root Mean Squared Error	Square root of the average of the squared errors.
6	2023-05-11	2023	2023-kaggle-ai-report	2023 Kaggle AI Report	Essays on the state of machine learning in 2023	Mean Absolute Error	Mean absolute error regression loss. See https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html
7	2023-05-04	2023	iclr-2023-kagglezindi	ICLR 2023 Kaggle/Zindi Hackathon	Predict CO2 Emissions	Root Mean Squared Error	Square root of the average of the squared errors.
8	2023-05-02	2023	playground-series-s3e14	Prediction of Wild Blueberry Yield	Playground Series - Season 3, Episode 14	Mean Absolute Error	Average of the absolute value of each individual error.
9	2023-04-18	2023	playground-series-s3e13	Classification with a Tabular Vector Borne Disease Dataset	Playground Series - Season 3, Episode 13	MAP@{K}	Mean Average Precision at K
10	2023-04-11	2023	image-matching-challenge-2023	Image Matching Challenge 2023	Reconstruct 3D scenes from 2D images	imc2023	None
11	2023-04-04	2023	playground-series-s3e12	Binary Classification with a Tabular Kidney Stone Prediction Dataset	Playground Series - Season 3, Episode 12	sklearn_roc_auc_score	None
12	2023-03-23	2023	fathomnet-out-of-sample-detection	FathomNet 2023	Shifting seas, shifting species: Out-of-sample detection in the deep ocean	FathomNet 2023	None
13	2023-03-21	2023	benetech-making-graphs-accessible	Benetech - Making Graphs Accessible	Use ML to create tabular data from graphs	Benetech Mixed Data Type Matching Score	A mean-similarity index on [0, 1] with instances as either string or numeric vectors.
14	2023-03-20	2023	playground-series-s3e11	Regression with a Tabular Media Campaign Cost Dataset	Playground Series - Season 3, Episode 11	Mean Squared Log Error	Mean squared logarithmic error regression loss. See https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_log_error.html
15	2023-03-15	2023	vesuvius-challenge-ink-detection	Vesuvius Challenge - Ink Detection	Resurrect an ancient library from the ashes of a volcano	DiceFBeta	Mean Dice Coefficient with F-Score Beta parameter
16	2023-03-09	2023	tlvmc-parkinsons-freezing-gait-prediction	Parkinson's Freezing of Gait Prediction	Event detection from wearable sensor data	sklearn_average_precision_score	None
17	2023-03-07	2023	birdclef-2023	BirdCLEF 2023	Identify bird calls in soundscapes	buffered_cmAP	None
18	2023-03-07	2023	playground-series-s3e10	Binary Classification with a Tabular Pulsar Dataset	Playground Series - Season 3, Episode 10	Log Loss	Log Loss
19	2023-02-28	2023	playground-series-s3e9	Regression with a Tabular Concrete Strength Dataset	Playground Series - Season 3, Episode 9	Root Mean Squared Error	Square root of the average of the squared errors.

	Id	Title	MaxDailySubmissions	NumScoredSubmissions	MaxTeamSize	BanTeamMergers	EnableTeamModels	RewardType	NumPrizes	UserRankMultiplier	CanQualifyTiers	TotalTeams	TotalCompetitors	TotalSubmissions
0	2408	Forecast Eurovision Voting	5	5	20	False	False	USD	1	1.0	False	22	25	22
1	2435	Predict HIV Progression	4	4	20	False	False	USD	1	1.0	True	107	116	855
2	2438	World Cup 2010 - Take on the Quants	5	5	20	False	False	USD	1	NaN	False	0	0	0
3	2439	INFORMS Data Mining Contest 2010	5	5	20	False	False	USD	1	1.0	True	145	153	1483
4	2442	World Cup 2010 - Confidence Challenge	5	5	20	False	False	USD	1	NaN	False	63	64	63

	Id	Title	Slug	Subtitle	MaxDailySubmissions	NumScoredSubmissions	MaxTeamSize	BanTeamMergers	EnableTeamModels	RewardType	NumPrizes	UserRankMultiplier	CanQualifyTiers	TotalTeams	TotalCompetitors	TotalSubmissions
0	10385	Santander Customer Transaction Prediction	santander-customer-transaction-prediction	Can you identify who will make a transaction?	3	2	5	False	True	USD	5	1	True	8751	9787	104121
1	9120	Home Credit Default Risk	home-credit-default-risk	Can you predict how capable each applicant is of repaying a loan?	5	2	20	False	True	USD	3	1	True	7176	8373	131888
2	14242	IEEE-CIS Fraud Detection	ieee-fraud-detection	Can you detect fraud from customer transactions?	5	2	5	False	True	USD	3	1	True	6351	7389	125219
3	18599	M5 Forecasting - Accuracy	m5-forecasting-accuracy	Estimate the unit sales of Walmart retail goods	5	1	5	False	True	USD	5	1	True	5558	7022	88741
4	35332	American Express - Default Prediction	amex-default-prediction	Predict if a customer will default in the future	5	2	5	False	True	USD	4	1	True	4874	6003	90058

	Id	Slug	Title	Subtitle	HostSegmentTitle	ForumId	OrganizationId	EnabledDate	DeadlineDate	ProhibitNewEntrantsDeadlineDate	TeamMergerDeadlineDate	TeamModelDeadlineDate	ModelSubmissionDeadlineDate	FinalLeaderboardHasBeenVerified	HasKernels	OnlyAllowKernelSubmissions	HasLeaderboard	LeaderboardPercentage	LeaderboardDisplayFormat	EvaluationAlgorithmAbbreviation	EvaluationAlgorithmName	EvaluationAlgorithmDescription	EvaluationAlgorithmIsMax	MaxDailySubmissions	NumScoredSubmissions	MaxTeamSize	BanTeamMergers	EnableTeamModels	RewardType	RewardQuantity	NumPrizes	UserRankMultiplier	CanQualifyTiers	TotalTeams	TotalCompetitors	TotalSubmissions	ValidationSetName	ValidationSetValue	EnableSubmissionModelHashes	EnableSubmissionModelAttachments	HostName	CompetitionTypeId
0	9120	home-credit-default-risk	Home Credit Default Risk	Can you predict how capable each applicant is of repaying a loan?	Featured	35355	1536	05/17/2018 22:56:29	08/29/2018 23:59:00	08/22/2018 23:59:00	08/22/2018 23:59:00	09/14/2018 23:59:00	None	True	True	False	True	20	0.00000	AUC	Area Under Receiver Operating Characteristic Curve	Measures discrimination. Calculates how well an algorithm separates true positives from false positives. Overall good metric for classification problems. Has range [0.5, 1.0]	True	5	2	20	False	True	USD	NaN	3	1	True	7176	8373	131888	None	None	False	False	None	1
1	14242	ieee-fraud-detection	IEEE-CIS Fraud Detection	Can you detect fraud from customer transactions?	Research	275877	3016	07/15/2019 20:47:11	10/03/2019 23:59:00	10/03/2019 23:59:00	09/24/2019 23:59:00	10/26/2019 00:00:00	None	True	True	False	True	20	0.000000	AUC	Area Under Receiver Operating Characteristic Curve	Measures discrimination. Calculates how well an algorithm separates true positives from false positives. Overall good metric for classification problems. Has range [0.5, 1.0]	True	5	2	5	False	True	USD	NaN	3	1	True	6351	7389	125219	None	None	False	False	None	1
2	10385	santander-customer-transaction-prediction	Santander Customer Transaction Prediction	Can you identify who will make a transaction?	Featured	130117	141	02/13/2019 23:00:00	04/10/2019 23:59:00	04/03/2019 23:59:00	04/03/2019 23:59:00	05/01/2019 00:00:00	None	True	True	False	True	25	0.00000	AUC	Area Under Receiver Operating Characteristic Curve	Measures discrimination. Calculates how well an algorithm separates true positives from false positives. Overall good metric for classification problems. Has range [0.5, 1.0]	True	3	2	5	False	True	USD	NaN	5	1	True	8751	9787	104121	None	None	False	False	None	1

Meta Kaggle Dataset Analysis - 2 - Competitions¶

5. Competitions¶

Competitions per year¶

Submissions¶

Top 5 competitions based on Total competitors¶

Competition that received max submissions¶

All competitions¶

	Title	Subtitle
0	"""Clever"" questions classification"	The challenge is distinguish questions prepared by professional authors from questions suggested by game users.
1	"""Сириус""	зима 2019"
2	"2021 Academic Competition ""Uplift Modeling"""	Rank clients according to their purchasing responsiveness to communication
3	"STAT6031 ""Crime"" Project"	Course Project in Fall 2019
4	"SejongAI.텀프로젝트.[""범죄유형예측""]"	세종대학교 인공지능 텀 프로젝트 [18011863]_[최영민]
5	"Узнай героев сериала ""Друзья"" с первых слов"	"В задании необходимо научиться предсказывать, кому из 6 главных героев сериала ""Друзья"" принадлежит реплика."
6	"אכ""אתון"	חיזוי נשר מיטב
7	"אכ""אתון"	חיזוי נשר מיטב
8	#HomeAlone2020	Welcome all for #homeAlone2020 kaggle competiton.
9	#HomeAlone2020	Welcome all for #homeAlone2020 kaggle competiton.
10	( Recognizance - 2 ) Power Lines Detection	Detect the images containing the power lines from set of given visible and infrared images.
11	(Deprecated)Crime Category Classification using DR	Predict the category of crimes that occurred in the city by the bay
12	(Recognizance-1) Electricity Bill Fraud Detection	Detect the fraudulent clients of electricity consumption using their electrical billing history.
13	(SIT × DS)In-lecture competition 2022	Banking Dataset Marketing Targets
14	(aborted)	Error
15	....2319...	MME
16	0.00000	clustering task
17	09-860 A4: Machine Learning for Mol. Sci. HW1	Let's fight COVID-19 together!
18	0th datascience competition	Let's join and enjoy this competition!
19	1026	None
20	10316 Materials Design with ML and AI (Jan 22)	In-class machine learning competition
21	104-1 MLDS_Final	Visual Question Answering
22	104-1 MLDS_HW2	Recurrent Neural Network
23	104-1 MLDS_HW3	Structure Learning
24	1040	None
25	108-1 NTUT Building Deep Learning Applications HW1	Classifying Handwritten Letters and Digits in Extended MNIST dataset
26	108-1 NTUT DL APP HW2 Image Hashtag Recommendation	Recommend Most Relevant Hashtags based on the Content of Images
27	108-1 NTUT Machine Learning Homework 1	Classifying Handwritten Letters and Digits in Extended MNIST dataset
28	108-1 NTUT Machine Learning Homework 2	Recommend Most Relevant Hashtags based on the Content of Images
29	108-2 NTUT Building Deep Learning Applications HW1	Classifying Handwritten Letters and Digits in Extended MNIST dataset
30	108-2 NTUT DL APP HW2 Image Hashtag Recommendation	Recommend Most Relevant Hashtags based on the Content of Images
31	108-2 NTUT DRL HW3 VizDoom	VizDoom Competition
32	108-2-NTUT-DRL-HW1	NTUT Deep Reinforcement Learning Homework 1: Frozen Lake
33	109-1 NTUT Building Deep Learning Applications HW1	Classifying Handwritten Letters and Digits in Extended MNIST dataset
34	109-1 NTUT DL APP HW2 Image Hashtag Recommendation	Recommend Hashtags based on the Content of Images
35	109-2 Machine Learning	CNN for image classification
36	109-2 NTUT Building Deep Learning Applications HW1	Classifying Handwritten Letters and Digits in Extended MNIST dataset
37	109-2 NTUT DL APP HW2 - Image Hashtags	Recommending Hashtags based on the Image Content
38	109-2 UTA DL APP HW1	Classifying Handwritten Letters and Digits in Extended MNIST dataset
39	109-2 UTA DL APP HW2 Image Hashtag Recommendation	Recommend Hashtags based on the Content of Images