Update README.md

Author: hyunjoonbok

README.md

@@ -41,28 +41,28 @@ Below examples include the intense usage of industry-hot frameworks (i.e. Pytorc
    <p>
    Finding a customer who's income level. Simple ML Classification problem tackled with Fast.ai API. Executable to almost all types of tabular data to naively achieve a good baseline model in a few lines of code. Also, collaborative filtering is when you're tasked to predict how much a user is going to like a certain item.  Here I looked at "MovieLens" dataset to predict the rating a user would give a particular movie (from 0 to 5) 
 	</p>
-   December 10, 2019
+   May 10, 2018
 
 
    ### [(Kaggle) M5_Forecasting](https://github.com/hyunjoonbok/Python-Projects/blob/master/Kaggle/%5BKaggle%5D%20M5_Forecasting.ipynb): 
    <p>
    From Walmart sales data, forecast daily sales for the next 28 days. The data, covers stores in three US States (California, Texas, and Wisconsin) and includes item level, department, product categories, and store details. Pre-process (Feature Enginenering / Hyperparameter Optimization) given data and used LGB/XGB ensemble to generate a final submission. From [M5 Forecasting - Accuracy](https://www.kaggle.com/c/m5-forecasting-accuracy/overview) in Kaggle. 
 	</p>
-   December 10, 2019	
+   Mar 24, 2020	
 
 
    ### [(Kaggle) NCAAW® 2020 ML Competition](https://github.com/hyunjoonbok/Python-Projects/blob/master/Kaggle/%5BKaggle%5D%20NCAAW20.ipynb): 
    <p>
    To forecast the outcomes of March-Madness during rest of 2020's NCAAW games. Covers all team-by-team season games results data. Pre-processing of tabular data and ensemble of LGB/XGB generates a final submission. From [Google Cloud & NCAA® ML Competition 2020-NCAAW](https://www.kaggle.com/c/google-cloud-ncaa-march-madness-2020-division-1-womens-tournament/overview) in Kaggle.  *Update: this competition was cancelled in Mar.2020 due to the COVID-19.*
 	</p>
-   December 10, 2019
+   Feb 6, 2020
 
 
-   ### [(Kaggle) NY Taxi Trip Duration](https://github.com/hyunjoonbok/Python-Projects/blob/master/Kaggle/%5BKaggle%5D%20NCAAW20.ipynb):
+   ### [(Kaggle) NY Taxi Trip Duration](https://github.com/hyunjoonbok/Python-Projects/blob/master/Kaggle/%5BKaggle%5D%20NY%20Taxi%20Data.ipynb):
    <p>
    To Forecast total ridetime of taxi trips in New York City. Covers both Fast.ai and LGB version of solving the problem. From [New York City Taxi Trip Duration](https://www.kaggle.com/c/nyc-taxi-trip-duration) in Kaggle. 
 	</p>
-   December 10, 2019
+   August 5, 2019
 
 
 <hr>
@@ -74,39 +74,39 @@ Below examples include the intense usage of industry-hot frameworks (i.e. Pytorc
    <p>
    Use Fast.ai framework to load image data, create generator/discriminator from images. Then create a model with a custom GAN loss function. Check error and improve on test image sets.
 	</p>
-   December 10, 2019
+   June 13, 2019
 
    ### [DCGAN - Generate_Fake_Images](https://github.com/hyunjoonbok/Python-Projects/blob/master/Fast.ai/(Fast.ai)%20DCGAN%20-%20Generate_new_images.ipynb): 
    <p>
    Based on a set of celebrity images, we are generating a new set of fake images. Then compare Real Images vs. Fake Images create generator/discriminator from images. Used Pytorch to load image / create Generator/Discriminator and training loop. 
 	</p>
-   December 10, 2019
+   June 24, 2019
 
    ### [MNIST CNN, Skip-connection (U-NET)](https://github.com/hyunjoonbok/Python-Projects/blob/master/Fast.ai/(Fast.ai)%20MNIST%20CNN%2C%20Skip-connection%20(U-NET).ipynb): 
    <p>
    Use Fast.ai framework that's built on top of pytorch, to build a simple MNIST CNN model. Use Skip-connection to build a simpel conv-nn, which achieve a state-of-the-art result (99.6% accuracy on test-set).
 	</p>
-   December 10, 2019
+   June 30, 2019
 
 
    ### [(Kaggle) Handwritten_Image_Classification (Grapheme language)](https://github.com/hyunjoonbok/Python-Projects/blob/master/Fast.ai/%5BKaggle%5D%20(Fast.ai)%20Handwritten_Image_Classification%20(Grapheme%20language).ipynb): 
    <p>
    Use Fast.ai to build a CNN model to classify a image of a langauge. From [Bengali.AI Handwritten Grapheme Classification](https://www.kaggle.com/c/bengaliai-cv19) in Kaggle. Includes Load image / Genearte custom loss function / Train & Test data using Fast.ai.
 	</p>
-   December 10, 2019
+   Jan 3, 2020
 
    ### [Simple CNN data Augmentation](https://github.com/hyunjoonbok/Python-Projects/blob/master/Fast.ai/(Fast.ai)%20Simple%20CNN%20data%20Augmentation.ipynb): 
    <p>
    Image-Augmentation on CNN model is one of the most important feature engineering steps. Here I looked at how image tranformation can be done with a built-in. Wider range of selection are availalbe in [fast.ai-vision-transform](https://docs.fast.ai/vision.transform.html) except the ones shown.
    *Things to add*: How ["Albumentation"](https://github.com/albumentations-team/albumentations) library can be used within Fast.ai framework.   
 	</p>
-   December 10, 2019
+   Nov 12, 2019
 
    ### [(Kaggle) MNIST Digit Recognizer](https://github.com/hyunjoonbok/Python-Projects/blob/master/Fast.ai/%5BKaggle%5D%20(Fast.ai)%20MNIST%20Digit%20Recognizer%20Kaggle.ipynb): 
    <p>
    Kaggle version of MNIST. Use Fast.ai and transfer learning to solve. 
 	</p>
-   December 10, 2019
+   December 5, 2017
 
 
 <hr>
@@ -118,13 +118,13 @@ Below examples include the intense usage of industry-hot frameworks (i.e. Pytorc
    <p>
    Using Fast.ai to expand a tabular data to utilize many of columns in order to predict sales on stroes based on different situations like promotion, seaons, holidays, etc. Insights are from [Rossmann Store Sales](https://www.kaggle.com/c/rossmann-store-sales)
 	</p>
-   December 10, 2019
+   December 5, 2015
 
    ### [(Kaggle) M5_Forecasting](https://github.com/hyunjoonbok/Python-Projects/blob/master/Kaggle/%5BKaggle%5D%20M5_Forecasting.ipynb):
    <p>
    From Walmart sales data, forecast daily sales for the next 28 days. The data, covers stores in three US States (California, Texas, and Wisconsin) and includes item level, department, product categories, and store details. Pre-process (Feature Enginenering / Hyperparameter Optimization) given data and used LGB/XGB ensemble to generate a final submission. From [M5 Forecasting - Accuracy](https://www.kaggle.com/c/m5-forecasting-accuracy/overview) in Kaggle.
 	</p>
-   December 10, 2019
+   Mar 24, 2020
 
 
 <hr>
@@ -137,31 +137,31 @@ Below examples include the intense usage of industry-hot frameworks (i.e. Pytorc
    <p>
    2-way polarity (positive, negative) classification system for tweets. Using Fast.ai framework to fine-tune a language model and build a classification model with close to 80% accuracy. 
 	</p>
-   December 10, 2019
+   Feb 21, 2020
 
    ### [BERT-base: classify twitter sentiment](https://github.com/hyunjoonbok/Python-Projects/blob/master/Pytorch/(Pytorch)%20(2)%20BERT-base%20to%20Classify%20Twitter%20NLP.ipynb): 
    <p>
    Used Pytorch to encode/tokenize/train/evaluate model. The most simple version
 	</p>
-   December 10, 2019
+   December 5, 2019
 
    ### [BERT-large: classify twitter sentiment](https://github.com/hyunjoonbok/Python-Projects/blob/master/Pytorch/(Pytorch)%20(3)%20BERT-large%20to%20Classify%20Twitter%20NLP.ipynb): 
    <p>
    Using large BERT (takes longer)
 	</p>
-   December 10, 2019
+   December 7, 2019
 
    ### [Google-play App Review Sentiment Analysis with BERT](https://github.com/hyunjoonbok/Python-Projects/blob/master/Pytorch/(Pytorch)%20Sentiment%20Analysis%20with%20Transformer%20BERT.ipynb): 
    <p>
    3-way polarity (positive, neutral, negative) sentiment analysis system for Google-Play App reviews. Use Pytorch to get review in JSON, data-preprocess, Create pytorch dataloader , train/evaluate the model. Evaluate the errors and testing on the raw text data in the end.
 	</p>
-   December 10, 2019
+   Mar 5, 2020
 
    ### [(Kaggle) Handwritten_Image_Classification (Grapheme language)](https://github.com/hyunjoonbok/Python-Projects/blob/master/Pytorch/%5BKaggle%5D%20(Pytorch)%20Handwritten_Image_Classification%20(Grapheme%20language).ipynb): 
    <p>
    Pytorch version of builing a CNN model to classify a image of a langauge. Complete model building from loading/defining/transforming data to create and train model. From [Bengali.AI Handwritten Grapheme Classification](https://www.kaggle.com/c/bengaliai-cv19) in Kaggle. 
 	</p>
-   December 10, 2019
+   Jan 4, 2020
 
 
 <hr>
@@ -173,7 +173,7 @@ Below examples include the intense usage of industry-hot frameworks (i.e. Pytorc
    <p>
    Use of Python language to pull data directly from ELK stack. Origianlly came in to JSON format, convert it to Dataframe and do simple EDA / Visualization.
 	</p>
-   December 10, 2019
+   December 28, 2019
 
 
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