Ram Seshadri
rsesha
Data Scientist, Instructor, AI Consultant
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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.
AutoViz pipeline example for Orchest.io
Anomaly detection algorithm implementation in Python
An optimizer that transitions from SGD to Adam via weighted average of calculated steps
Complete-Life-Cycle-of-a-Data-Science-Project
Loan portfolio analysis on Lending Club's publicly available datasets
Repositories
45This is a new search agent that searches multiple search engines in parallel and delivers a summarized answer similar to "AI Overviews".
My copy of trend following strategies from the python book of the same name.
MyFinGPT: My custom version of FinGPT: Open-Source Financial Large Language Models for all
OpenFE: automated feature generation with expert-level performance
Anomaly detection algorithm implementation in Python
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.
AutoViz pipeline example for Orchest.io
An optimizer that transitions from SGD to Adam via weighted average of calculated steps
Complete-Life-Cycle-of-a-Data-Science-Project
No description provided.
Loan portfolio analysis on Lending Club's publicly available datasets
Awesome Orchest projects, both official and submitted by the community.
An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
No description provided.
No description provided.
Rectified Adam + Adabelief optimizer for tf.keras
A neural network hyper parameter tuner
Baseball data analysis in Python
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.
higher-level NLP built on spaCy
Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
All the code for a series of Medium articles on Approximate Nearest Neighbors
An Elegant Neural Network User Interface to build drag-and-drop neural networks, train in the browser, visualize during training, and export to Python.
starter from "How to Train a GAN?" at NIPS2016
Top 10 in MachineHack | Top 80 in AnalyticsVidya & Zindi | Hack AI
No description provided.
Course in Probabilistic Programming in Python for the 2018 EU Summer School
2018 CQS Summer Institute course in machine learning
Advanced Statistical Computing at Vanderbilt University's Department of Biostatistics
Introduction to Statistical Modeling with Python (PyCon 2017)