Pinned Repositories
awesome-python
A curated list of awesome Python frameworks, libraries and software
cheatsheets
RStudio Cheat Sheets
Data-Science-45min-Intros
Ipython notebook presentations for getting starting with basic programming, statistics and machine learning techniques
data-science-from-scratch
code for Data Science From Scratch book
data-science-ipython-notebooks
Continually updated data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines. https://bit.ly/data-notes
data-science-your-way
Ways of doing Data Science Engineering and Machine Learning in R and Python
data_science_python
Source code for the "Practical Data Science in Python" tutorial
datascience
A Python library for introductory data science
DataScienceSpecialization.github.io
http://DataScienceSpecialization.github.io
Introduction-to-Data-Science
an introduction to Data Science using Python
kvamarnath's Repositories
kvamarnath/cheatsheets
RStudio Cheat Sheets
kvamarnath/machine-learning-mindmap
A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.
kvamarnath/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials
A comprehensive list of ML tutorials - initially originally created by Donne Martin and will be expanded in areas of AI/Deep Learning / Machine Vision and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by me.
kvamarnath/aws-setup
kvamarnath/BigDL
BigDL: Distributed Deep Learning Library for Apache Spark
kvamarnath/cheatsheets-ai
Essential Cheat Sheets for deep learning and machine learning researchers
kvamarnath/cs229
Stanford CS229 (Autumn 2017)
kvamarnath/DataAnalytics_With_R
Data Analytics with R workshop at Vardhaman College Of Engineering
kvamarnath/gans
Generative Adversarial Networks implemented in PyTorch and Tensorflow. More coming soon...
kvamarnath/intro2stats
Introduction to Statistics using Python
kvamarnath/Links_and_Articles
Links to articles that I find interesting
kvamarnath/MacHack
Tips and Tricks that I've learnt on Mac
kvamarnath/MachineLearning_Python
kvamarnath/minigo
An open-source implementation of the AlphaGoZero algorithm
kvamarnath/ML-From-Scratch
Python implementations of Machine Learning models and algorithms from scratch. Aims to cover everything from Data Mining techniques to Deep Learning.
kvamarnath/pipeline
PipelineAI: End-to-End ML and AI Platform for Real-time Spark and Tensorflow Data Pipelines
kvamarnath/PythonDataScienceHandbook
Jupyter Notebooks for the Python Data Science Handbook
kvamarnath/randomized-SVD
demos for PyBay talk: Using Randomness to make code faster
kvamarnath/RNNLG
RNNLG is an open source benchmark toolkit for Natural Language Generation (NLG) in spoken dialogue system application domains. It is released by Tsung-Hsien (Shawn) Wen from Cambridge Dialogue Systems Group under Apache License 2.0.
kvamarnath/rouseguy.github.io
My website
kvamarnath/scikit-learn-videos
Jupyter notebooks from the scikit-learn video series
kvamarnath/scipy-2017-sklearn
Scipy 2017 scikit-learn tutorial by Alex Gramfort and Andreas Mueller
kvamarnath/sklearn-classification
Data Science Notebook on a Classification Task, using sklearn and Tensorflow.
kvamarnath/sklearn_tutorial
Materials for my scikit-learn tutorial
kvamarnath/strata_ny_2017_recommender_tutorial
Jupyter Notebooks for Strata Data Conference NY 2017 Deep Learning for Recommender Systems Tutorial
kvamarnath/Technical_Book_DL
This note presents in a technical though hopefully pedagogical way the three most common forms of neural network architectures: Feedforward, Convolutional and Recurrent.
kvamarnath/tennis_atp
ATP Tennis Rankings, Results, and Stats
kvamarnath/TensorFlow-Examples
TensorFlow Tutorial and Examples for Beginners with Latest APIs
kvamarnath/TensorFlow-Tutorials-1
Simple tutorials using Google's TensorFlow Framework
kvamarnath/tensorflow_tutorials
From the basics to slightly more interesting applications of Tensorflow