Sundaram Surampudi's starred repositories
alpaca-lora
Instruct-tune LLaMA on consumer hardware
ydata-profiling
1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
fecon235
Notebooks for financial economics. Keywords: Jupyter notebook pandas Federal Reserve FRED Ferbus GDP CPI PCE inflation unemployment wage income debt Case-Shiller housing asset portfolio equities SPX bonds TIPS rates currency FX euro EUR USD JPY yen XAU gold Brent WTI oil Holt-Winters time-series forecasting statistics econometrics
hailo_model_zoo
The Hailo Model Zoo includes pre-trained models and a full building and evaluation environment
relataly-public-python-tutorials
Beginner-friendly collection of Python notebooks for various use cases of machine learning, deep learning, and analytics. For each notebook there is a separate tutorial on the relataly.com blog.
IPython_notebooks
Set of Jupyter (iPython) notebooks (and few pdf-presentations) about things that I am interested on, like Computer Science, Statistics and Machine-Learning, Artificial Intelligence (AI), Financial Engineering, Optimization, Stochastic Modelling, Time-Series forecasting, Science in general... and more.
TimeSeries_Notebooks_Collections
Jupyter Notebooks Collection for Learning Time Series Models
how-to-train-your-neural-net
Deep learning research implemented on notebooks using PyTorch.
timeseries-notebooks
Hello world univariate examples for a variety of time series packages.
sentimentarcs_notebooks
SentimentArcs: a large ensemble of dozens of sentiment analysis models to analyze emotion in text over time
Time-series-forecasting-using-Deep-Learning
The goal of this notebook is to implement and compare different approaches to predict item-level sales at different store locations.
Time-series-Preprocessing-Studio-in-Jupyter
Time-series Data Preprocessing Studio in Jupyter notebook.
scipy_pandas_tutorial
Repository containing notebooks for my Pandas tutorial at SciPy India 2013
Predicting-Stock-Prices-Using-FB-Prophet
Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well. In this notebook I'm going to try forecasting Google stock price using facebook's prophet model.
Signal_Processing_Course
Essential topics of Signal Processing and their codes in python language in Jupyter Notebook.
colombia_covid_19_pipe
Pipeline to get data sources from Instituto Nacional de Salud - INS related to Covid19 cases daily report in Colombia to create datasets.
SpiffyDuck
A companion to the blog post "Build your own Low-Code Business Applications with SpiffWorkflow"
Tensorflow-Specialization
This repository contains course notebooks from the TensorFlow for practice specialization on Coursera