sharomin Goto Github PK

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code_snippets

convolutional-neural-networks-for-sentence-classification

In this project, we attempt to reproduce and improve the results achieved by Yoon Kim (2014), in [1] with no published code. We implement the proposed models in his paper, which describes sentence classifiers using CNN on top of pre-trained word vectors, Word2Vec. Classification in [1], is performed on multiple datasets, with static and minimally fine-tuned Word2Vec, feeding a single layer CNN. To improve the state-of-the-art, both static and fine-tuned word vectors are used in 2 separate channels to classify sentences[1]. In this work, we simplify Kim’s approach and instead focus only on the use of different kernel sizes with parallel layers. We see that the skill of the model on the unseen test dataset was very impressive, achieving 89%, which is above the skill of the model reported in the 2014 paper. We observed, fine-tuning the pre-trained vectors for specific task improves accuracy over static vectors, and we were able to reach accuracy mentioned in [1] for MR dataset.

csv-pickle-transition

pickle to csv and csv to pickle transition in Python

data_structure_and_algorithm

imdb-sentiment-analysis

Sentiment analysis on IMDb movie reviews

imdb-sentiment-analysis-

In this project, I have developed models to predict the sentiment of IMBD reviews. IMDB is a popular website and database of movie information and reviews (https://www.imdb.com/). The goal is to classify IMBD reviews as positive or negative based on the language they contain. This project was done for competing with other groups in www.kaggle.com to achieve the best accuracy in a competition.

ml-project-3

Machine Learning mini project 3

mnist-competition

mnist competition submission repo for Tensorflow KR group

modified-mnist

Image Processing Project which the goal was to perform an image analysis prediction challenge. The task is based upon the MNIST dataset (https://en.wikipedia.org/wiki/MNIST_database). The original MNIST contains handwritten numeric digits from 0-9 and the goal is to classify which digit is present in an image. Here, I worked with a Modified MNIST dataset. In this modified dataset, the images contain more than one digit and the goal was to find which number occupies the most space in the image. Each example is represented as a 64 × 64 matrix of pixel intensity values (i.e., the images are grey-scale not color).

natural-language-processing-nlp---quality-evaluation-system-based-on-comments

In this project we will use linear regression to predict the popularity of comments on Reddit. Reddit (www.reddit.com) is a popular website where users can form interest-based communities, post content (e.g., images, links to news articles), and participate in thread-based discussions.

reno-viva-api

Back-End API for renoViva webApp

reno-viva-webapp

sharomin.github.io

sharomin.tech

My Personal website