akshayratnawat Goto Github PK
Name: Akshay Ratnawat
Type: User
Bio: A data scientist who brings in a unique combination of deep technical expertise, analytics experience, story telling.
Location: Chicago, IL, USA
Name: Akshay Ratnawat
Type: User
Bio: A data scientist who brings in a unique combination of deep technical expertise, analytics experience, story telling.
Location: Chicago, IL, USA
Model Training for Maxim AI Devices
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
A curated list of awesome Deep Reinforcement Learning resources.
Reinforcement learning resources curated
Awesome Webots
This project explains why and how the Bagging algorithm is better. Bagged Models have tighter confidence intervals and are less biased in comparison to the full model
This project explains why and how are the Bagged Models better than the Complete Model. Bagged Model parameters have tighter confidence interval and a lower bias.
Understand similarities and differences between two different boroughs in Toronto and New York respectively. And find the best neighborhoods for office location for Fortune 500 companies.
This project explores the working of various Boosting algorithms and analyzes the results across different algorithms. Algorithms Used are: Random Forest, Ada Boost, Gradient Boost and XG Boost
This project shows how we can create a CNN to build an Image Classifier.
The model helps in predicting toxicity of Online comments, trained on Wikipedia comments data using Deep Neural Network (GRU+ GLoVe ))
TUM_ARL_SS16: With its proximity sensors the ePuck robot can estimate the relative position of a small box and learn to push it in a given direction using Reinforcement Learning.
Contains sample python codes for general topics.
This file explores the working of various Gradient Descent Algorithms to reach a solution. Algorithms used are: Batch Gradient Descent, Mini Batch Gradient Descent, and Stochastic Gradient Descent
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
The project explores KMeans and Gaussian Mixture Algorithms to classify the Boston Housing Dataset into different groups based on different parameters.
Latent Class Analysis on German Credit Data Set to find the latent variables affecting the credit outcomes and behaviour
The codes of paper "Long Text Generation via Adversarial Training with Leaked Information" on AAAI 2018. Text generation using GAN and Hierarchical Reinforcement Learning.
Application of simple and multiple linear regression. It also includes RFE and Gradient Descent Method for simple and multiple regression
Using PCA to reduce the dimension of data and for factor Analysis on Boston Housing Data
USe PCA and other segmentation techniques to find the focus areas for the Chilean Government to increase tourism competitiveness based on different city characteristics data.
Course material for an intro to programming class for analytics students
A collection of various RL algorithms like policy gradients, DQN and PPO. The goal of this repo will be to make it a go-to resource for learning about RL. How to visualize, debug and solve RL problems. I've additionally included playground.py for learning more about OpenAI gym, etc.
The objective is to teach robot to find and reach the target object in the minimum number of steps and using the shortest path and avoiding any obstacles such as humans, walls, etc usinf reinforcement learning algorithms.
This project is on building a real-time intelligent system which involves building and deploying a model to provide real-time model decisions from the real-time data stream.
Code repository for my course on the fundamentals of reinforcement learning
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.