Pinned Repositories
AdaSmooth
An optimizer that transitions from SGD to Adam via weighted average of calculated steps
adventures-with-ann
All the code for a series of Medium articles on Approximate Nearest Neighbors
Anomaly-Detection
Anomaly detection algorithm implementation in Python
Auto_TS
autoviz_pipeline
AutoViz pipeline example for Orchest.io
Complete-Life-Cycle-of-a-Data-Science-Project
Complete-Life-Cycle-of-a-Data-Science-Project
deep_autoviml_pipeline
Quick tutorial on orchest.io that shows how to build multiple deep learning models on your data with a single line of code using the popular python library, Deep AutoViML.
lending_club_analysis
Loan portfolio analysis on Lending Club's publicly available datasets
pydata-book
Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
Top-Correlations-to-a-Name
This function draws a correlation chart of the top "x" rows of a data frame that are highly correlated to a selected row in the dataframe. CAUTION: MAKE SURE YOU DIFFERENCE YOUR TIME SERIES DATA BEFORE DOING THIS. OTHERWISE, YOU'LL GET SPURIOUS CORRELATIONS! You can think of the rows of the input dataframe as containing rows with labels and the columns should contain time series data of returns or flows or change in sales over multiple time periods. Now this program will allow you to select the top 5 or 10 rows that are highly correlated to a given row selected by the column: column_name and using a search string "searchstring". The program will search for the search string in that column column_name and return a list of 5 or 10 rows that are the most correlated to that selected row. If you give "top" as a float ratio then it will use the ratio as the cut off point in the correlation coefficient to select rows.
rsesha's Repositories
rsesha/deep_autoviml_pipeline
Quick tutorial on orchest.io that shows how to build multiple deep learning models on your data with a single line of code using the popular python library, Deep AutoViML.
rsesha/autoviz_pipeline
AutoViz pipeline example for Orchest.io
rsesha/AdaSmooth
An optimizer that transitions from SGD to Adam via weighted average of calculated steps
rsesha/adventures-with-ann
All the code for a series of Medium articles on Approximate Nearest Neighbors
rsesha/Complete-Life-Cycle-of-a-Data-Science-Project
Complete-Life-Cycle-of-a-Data-Science-Project
rsesha/lending_club_analysis
Loan portfolio analysis on Lending Club's publicly available datasets
rsesha/pydata-book
Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
rsesha/Top-Correlations-to-a-Name
This function draws a correlation chart of the top "x" rows of a data frame that are highly correlated to a selected row in the dataframe. CAUTION: MAKE SURE YOU DIFFERENCE YOUR TIME SERIES DATA BEFORE DOING THIS. OTHERWISE, YOU'LL GET SPURIOUS CORRELATIONS! You can think of the rows of the input dataframe as containing rows with labels and the columns should contain time series data of returns or flows or change in sales over multiple time periods. Now this program will allow you to select the top 5 or 10 rows that are highly correlated to a given row selected by the column: column_name and using a search string "searchstring". The program will search for the search string in that column column_name and return a list of 5 or 10 rows that are the most correlated to that selected row. If you give "top" as a float ratio then it will use the ratio as the cut off point in the correlation coefficient to select rows.
rsesha/Auto_TS
rsesha/AutoViML
rsesha/baseball
Baseball data analysis in Python
rsesha/Bios8366
Advanced Statistical Computing at Vanderbilt University's Department of Biostatistics
rsesha/Coursera-ML-AndrewNg
use numpy, scipy, and tensorflow to implement these basic ML model and learning algorithm
rsesha/cqs_machine_learning
2018 CQS Summer Institute course in machine learning
rsesha/Data-Science-Hackathon-And-Competition
Top 10 in MachineHack | Top 80 in AnalyticsVidya & Zindi | Hack AI
rsesha/deep-learning-book
Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"
rsesha/ENNUI
An Elegant Neural Network User Interface to build drag-and-drop neural networks, train in the browser, visualize during training, and export to Python.
rsesha/FX-Trading-with-Python-and-Oanda
rsesha/ganhacks
starter from "How to Train a GAN?" at NIPS2016
rsesha/gcp-kfp-tutorial
rsesha/imbalanced-learn
Python module to perform under sampling and over sampling with various techniques.
rsesha/ISLR-python
An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
rsesha/orchest-examples
Awesome Orchest projects, both official and submitted by the community.
rsesha/PyMC3_EUSS
Course in Probabilistic Programming in Python for the 2018 EU Summer School
rsesha/Radabelief
Rectified Adam + Adabelief optimizer for tf.keras
rsesha/rsesha
rsesha/stochasticmutatortuner
A neural network hyper parameter tuner
rsesha/stockstats
Supply a wrapper ``StockDataFrame`` based on the ``pandas.DataFrame`` with inline stock statistics/indicators support.
rsesha/textacy
higher-level NLP built on spaCy
rsesha/twitterscraper
Scrape Twitter for Tweets