How to start a project in machine learning? How can I leverage all those techniques into real world challenge? There are my projects and competitions Jupyter notebooks for you as a starting points.
Table of content:
- 1. Enlighten Segmentation, July 2018
- 2. Objects detection and segmentation: Keras/Tensorflow/OpenCV
- 3. Sentiment Classifications for Hotel reviews by Deceptive Opinions: Python
- 4. Market Trend Prediction with Social Media Listening: Python/Keras/Facebook Graph API/Twitter API/ACHE Crawler/Beautifulsoup
- 5. Information Visualization Project - Business Cycle Introduction
- 6. Number of vehicles Prediction: scikit-learn/Keras
- 7. Recommendation System: scikit-learn/Surprise (under constructing)
- 8. Stock Price Forecasting by Stock Selections: Python/Tensorflow
This is a project which build up a pipeline line to enable research on image segmentation task based on Capsule Nets or SegCaps from scratch by Microsoft Common Objects in COntext (MS COCO) 2D image dataset.
The project delivery includes:
- Microsoft COCO dataset crawler program to automatic generate training data set for any class.
- You can choose any class of images from MS COCO dataset, the specific class of mask will also generate for segmentation task.
- You can base on image IDs to download image files and specify the annotation class you want for mask data.
Improve programs not only take computed tomography (CT) scan images, but also support 2D color images training and testing.
- A program captures image from video stream via a webcam for segmentation task.
Project address: https://github.com/Cheng-Lin-Li/SegCaps
The original paper for SegCaps can be found at https://arxiv.org/abs/1804.04241.
The official source code can be found at https://github.com/lalonderodney/SegCaps
Author’s project page for this work can be found at https://rodneylalonde.wixsite.com/personal/research-blog/capsules-for-object-segmentation.
This task is based on Mask RCNN (extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition) to perform transfer learning on Nuclear detection from variance image files.
My trial works to integrate threading webcam stream and the pre-trained for object detection and segmentation tasks.
Reference Paper: https://arxiv.org/abs/1703.06870
The original model clone from: https://github.com/matterport/Mask_RCNN
3. Sentiment Classifications for Hotel reviews by Deceptive Opinions: Python
This is a project to implement sentiment classifiers which implemented Naive Bayes (with Laplace smoothing), Vanilla and Averaged Perception models to classify the full text of the hotel reviews corpus, together with their truthful/deceptive and positive/negative labels.
Reference: Deceptive Opinion Spam Corpus v1.4
4. Market Trend Prediction with Social Media Listening: Python/Keras/Facebook Graph API/Twitter API/ACHE Crawler/Beautifulsoup
This project leveraged 1.5 years of 30 historical stock prices, Dow Jones Industrial Average(DJIA) index, with semantic information from social media (Facebook and Twitter) on T day to provide better one to many DJIA trend classifications for T+1/T+30 days than the model without social media info by LSTM in python a Keras.
Project Jupyter Notebook:
Github address: https://github.com/Cheng-Lin-Li/Market-Trend-Prediction
Build up a web application to introduce what business cycle is and how it will impact to us.
Project live demo site:Business Cycle Introduction
6. Number of vehicles Prediction: scikit-learn/Keras
This task is to perform prediction for number of vehicles by given data. This is a demo program to leverage four models (SVR, NN, LSTM, GRU) from existing libraries in one challenge. The final result can be improved by some emsemble techniques like Bootstrap aggregating (bagging), boosting, and stacking to get better performance.
Jupyter Notebook: Prediction Number of Vehicles
7. Recommendation System: scikit-learn/Surprise (under constructing)
This task leverages Content Based Filtering and Singular Value decomposition (SVD) to perform recommendation system build up.
This is a project which implemented Neural Network and Long Short Term Memory (LSTM) for stock price predictions. These models beat DJIA performance based on 1 quarter of weekly price, return rate of the DJIA components plus assistant indices to predict the highest increasing rate stock for the next quarter.