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Rishikesh Phatangare's Projects

evolution-strategies-exploration icon evolution-strategies-exploration

Contains implementation of: Tim Salimans Et al. “Evolution Strategies as a Scalable Alternative to Reinforcement Learning”. Arxiv.org. https://arxiv.org/pdf/1703.03864.pdf.

finrl icon finrl

A Deep Reinforcement Learning Library for Automated Trading in Quantitative Finance. NeurIPS 2020. 🔥

machine-learning-interview icon machine-learning-interview

Machine Learning Interviews from FAAG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.

marl icon marl

Multi-agent reinforcement learning framework

project_syncfusion_dashboard icon project_syncfusion_dashboard

This is a code repository for the corresponding YouTube video. In this tutorial we are going to build and deploy a an admin dashboard app using React.js and Syncfusion

reinforcement-learning icon reinforcement-learning

Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning

rl-adventure icon rl-adventure

Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL

stock-prediction-models icon stock-prediction-models

Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

stockpredictionai icon stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

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