siddamsetty srinath's repositories
Adam-experiments
Experiments with Adam/AdamW/amsgrad
ai-fundamentals
Code samples for AI fundamentals
awesome-gradient-boosting-papers
A curated list of gradient boosting research papers with implementations.
Best-Data-Science-Learning-Resources
My Study Collection data science courses, Article etc.
Bringing-Old-Photos-Back-to-Life
Bringing Old Photo Back to Life (CVPR 2020 oral)
data
Data and code behind the articles and graphics at FiveThirtyEight
data-science-interviews
Data science interview questions and answers
datapane
A gallery of Python scripts and reports #OpenAnalysis
Deep-Learning-1
A few notebooks about deep learning in pytorch
FES
Code and Resources for "Feature Engineering and Selection: A Practical Approach for Predictive Models" by Kuhn and Johnson
Hands-On-Image-Generation-with-TensorFlow-2.0
Hands-On Image Generation with TensorFlow 2.0, published by Packt
IBM-HR-Analytics-Employee-Attrition-Performance
The IBM HR Analytics Employee Attrition & Performance dataset from the Kaggle. I have first performed Exploratory Data Analysis on the data using various libraries like pandas,seaborn,matplotlib etc.. Then I have plotted used feature selection techniques like RFE to select the features. The data is then oversampled using the SMOTE technique in order to deal with the imbalanced classes. Also the data is then scaled for better performance. Lastly I have trained many ML models from the scikit-learn library for predictive modelling and compared the performance using Precision, Recall and other metrics.
machine-learning-books
this is a fork of collection of books for machine learning.
machine-learning-yearning
Translation of <Machine Learning Yearning> by Andrew NG
mit-15-003-data-science-tools
Study guides for MIT's 15.003 Data Science Tools
open-solution-home-credit
Open solution to the Home Credit Default Risk challenge :house_with_garden:
PythonDataScienceHandbook
Python Data Science Handbook: full text in Jupyter Notebooks
scratch_mlp
Explaining the Math of how neural networks learn
Spark-with-Python
Fundamentals of Spark with Python (using PySpark), code examples
stanford-cs-229-machine-learning
VIP cheatsheets for Stanford's CS 229 Machine Learning
The-Python-Workbook-Solutions
Solutions to The Python Workbook's exercises, written in Python 3.
theMLbook
The Python code to reproduce the illustrations from The Hundred-Page Machine Learning Book.
ThinkBayes
Code repository for Think Bayes.
ThinkBayes2
Text and code for the forthcoming second edition of Think Bayes, by Allen Downey.
transformers
🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.