psmishra7

abstractive_summarisation

Abstractive summarisation using Bert as encoder and Transformer Decoder

advertools

advertools - online marketing productivity and analysis tools

autogluon_mlflow

Notebook for autogluon + mlflow params

clickableImage

(Sogang) Yerago image processing: clickable image processing and save it into text file - python

graph_pull_from_google_analytics

Use Bokeh (https://bokeh.pydata.org/en/latest/) to graph docs feedback from Google Analytics

logistics_piping_giraph

code build for giraph extensions for semi-load

next_edge_predict_graph

Edge and edge growth predictions in communities and ecosystems (unmarked only)

cryptocurrency-price-prediction

Cryptocurrency Price Prediction Using LSTM neural network

CryptocurrencyPrediction

Predict Cryptocurrency Price with Deep Learning

feature-engineering-and-feature-selection

A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.

fq2

GE Flight Quest 2

geo-repositories-sg

list of resources for gathering geo data in singapore + other related stuff

map-website

Mercari-Price

pyspark-examples

Code examples on Apache Spark using python

rekognition

social network API linkage with recognition for neo4j

sequence

A Python module for looping over a sequence of commands with a focus on high configurability.

topological-sorting

Python module to compute the topological sorting of a directed graph, includes handling of cycles and loops.

Unsupervised-Features-Learning-for-Image-Classification

Recently, image classification draw attentions of many researchers. The need of object recognition grows drastically, especially in the context of biometric, biomedical imaging and real time scene understanding. Computer vision task is the most challenging in machine learning. For that reason, it's fundamental to tackle this concern using appropriate clustering and classification techniques. However, the quest for the best unsupervised features extraction remain an open problem even if CNNs reach a remarkable success, establishing new state-of-the-art. In this context, we study from an acute insight standpoint the standard clustering models K-means, GMM and Naive Bayes classification algorithm in order to draw conclusion and underline their limits for such complicated tasks. To what extent are k-means and GMM efficient ? Why they fail and how to circumvent their weaknesses.