leelaprabhu's Stars
tensorflow/tensorflow
An Open Source Machine Learning Framework for Everyone
scikit-learn/scikit-learn
scikit-learn: machine learning in Python
pandas-dev/pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
aymericdamien/TensorFlow-Examples
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
pytorch/examples
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
rlabbe/Kalman-and-Bayesian-Filters-in-Python
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
openai/spinningup
An educational resource to help anyone learn deep reinforcement learning.
fossasia/visdom
A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy.
EpistasisLab/tpot
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
tkarras/progressive_growing_of_gans
Progressive Growing of GANs for Improved Quality, Stability, and Variation
farizrahman4u/seq2seq
Sequence to Sequence Learning with Keras
afshinea/stanford-cs-221-artificial-intelligence
VIP cheatsheets for Stanford's CS 221 Artificial Intelligence
cbfinn/maml
Code for "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"
aws-solutions-library-samples/guidance-for-training-an-aws-deepracer-model-using-amazon-sagemaker
DeepRacer workshop content. This Guidance demonstrates how software developers can use an Amazon SageMaker Notebook instance to directly train and evaluate AWS DeepRacer models with full control
hse-aml/natural-language-processing
Resources for "Natural Language Processing" Coursera course.
robjhyndman/forecast
Forecasting Functions for Time Series and Linear Models
Kismuz/btgym
Scalable, event-driven, deep-learning-friendly backtesting library
farizrahman4u/recurrentshop
Framework for building complex recurrent neural networks with Keras
sjvasquez/web-traffic-forecasting
Kaggle | Web Traffic Forecasting 📈
wwrechard/pydlm
A python library for Bayesian time series modeling
chrisstroemel/Simple
Experimental Global Optimization Algorithm
locuslab/trellisnet
[ICLR'19] Trellis Networks for Sequence Modeling
arrigonialberto86/deepar
Tensorflow implementation of Amazon DeepAR
scottpletcher/deepracer
AWS DeepRacer Experimentation
CompVis/metric-learning-divide-and-conquer
Source code for the paper "Divide and Conquer the Embedding Space for Metric Learning", CVPR 2019
tcassou/causal_impact
Python package for causal inference using Bayesian structural time-series models.
xtma/pytorch_car_caring
Reinforcement Learning for Gym CarRacing-v0 with PyTorch
robjhyndman/M4metalearning
robjhyndman/fpp2-package
All data sets required for the examples and exercises in the book "Forecasting: principles and practice" (2nd ed, 2018) by Rob J Hyndman and George Athanasopoulos <http://OTexts.org/fpp2/>. All packages required to run the examples are also loaded.
zalandoresearch/probrnn
State space modeling with recurrent neural networks