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adv_fin_ml_exercises

Experimental solutions to selected exercises from the book [Advances in Financial Machine Learning by Marcos Lopez De Prado]

ai-blog

Accompanying repository for Let's make a DQN / A3C series.

auto-pytorch

Automatic architecture search and hyperparameter optimization for PyTorch

auto-sklearn

awesome-chatgpt-prompts

This repo includes ChatGPT prompt curation to use ChatGPT better.

awesome-datascience

:memo: An awesome Data Science repository to learn and apply for real world problems.

awesome-deep-trading

List of awesome resources for machine learning-based algorithmic trading

awesome-rl

Reinforcement learning resources curated

blackbox

btgym

Scalable, event-driven, deep-learning-friendly backtesting library

build-your-own-x

🤓 Build your own (insert technology here)

catboost

CatBoost is an open-source gradient boosting on decision trees library with categorical features support out of the box for Python, R

char-rnn

Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch

ckconv

Code repository of the paper "CKConv: Continuous Kernel Convolution For Sequential Data" https://arxiv.org/abs/2102.02611

cosnet

See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks (CVPR19)

coursera-test

Coursera test repository

cs231

Complete Assignments for Spring 2017 CS231n: Convolutional Neural Networks for Visual Recognition

cs231n-2017

Completed the CS231n spring 2017 assignments from Stanford university

cs231n-2017-summary

After watching all the videos of the famous Standford's CS231n course that took place in 2017, i decided to take summary of the whole course to help me to remember and to anyone who would like to know about it. I've skipped some contents in some lectures as it wasn't important to me.

data-engineer-roadmap

Roadmap to becoming a data engineer in 2021

data-engineering

Getting Started with Data Enngineering

data-science

:bar_chart: Path to a free self-taught education in Data Science!

datacamp_tutorials

dds

deep-learning-papers

Papers about deep learning ordered by task, date. Current state-of-the-art papers are labelled.

deep-reinforcement-learning-with-stock-trading

This project uses Deep Reinforcement Learning (DRL) to develop and evaluate stock trading strategies. By implementing agents like PPO, A2C, DDPG, SAC, and TD3 in a realistic trading environment with transaction costs, it aims to optimize trading decisions based on return, volatility, and Sharpe ratio.

deep-rl-keras

Keras Implementation of popular Deep RL Algorithms (A3C, DDQN, DDPG, Dueling DDQN)

deep-rl-seminars

Materials for Deep RL seminars.

deep-trading

Algorithmic trading with deep learning experiments