ayusha378

FixedIncomeQuantTrading

adversarial

Code and hyperparameters for the paper "Generative Adversarial Networks"

backtrading-python-binance

Backtesting several trading strategy and rank them according their profit return.

Bootcamp

Python + ML Bootcamp

Cs-231n

Visual recognition using CNN

cs224

My solutions to assignments for Natural Language processing-Stanford(CS 224n)

Datasets

JigglypuffRL

NLDC2020

Supplementary materials for the Nonlinear dynamics and Chaos course

NLP-Intro

Some introductory self learning in NLP

prodige

A supplementary code for Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs.

Quant

RL-Adventure

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

SAiDL-Summer-Assignment-2019

This repository contains my solutions to the SAiDL Summer Induction Assignment

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.

Term-Structure-Modelling-using-LSTM-Attention