Adrian Mocanu's repositories
milewski-ctfp-pdf
Bartosz Milewski's 'Category Theory for Programmers' unofficial PDF and LaTeX source
3d-game-shaders-for-beginners
🎮 A step-by-step guide on how to implement SSAO, depth of field, lighting, normal mapping, and more for your 3D game.
AI_for_Financial_Data
This is the code for "AI for Financial Data" By Siraj Raval on Youtube
anomaliesinoptions
In this notebook we will explore a machine learning approach to find anomalies in stock options pricing.
category-theory-as-a-tool-for-thought
Material for the "Category Theory as a Tool for Thought" talk
cwarp
Streamlit App and Notebook For CWARP
evaluate_genotick
Generate machine learning metrics from Genotick outputs
gym
A toolkit for developing and comparing reinforcement learning algorithms.
kaggle-two-sigma-winner
Kaggle Two Sigma 2nd Prize Winning Code https://www.kaggle.com/c/two-sigma-financial-news
Kaggle_Challenge_LIVE-Two-Sigma
This is the code for "Kaggle Challenge (LIVE)" by Siraj Raval on Youtube
qtpylib
QTPyLib, Pythonic Algorithmic Trading
reinforcement-learning
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
Reinforcement-learning-in-portfolio-management-
In this paper, we implement three state-of-art continuous reinforcement learning algorithms, Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO) and Policy Gradient (PG)in portfolio management.
shc-decoder-poc
Python SMART Health Cards (SHC) Decoder
shc-extractor
Extract the JSON payload from SHC QR codes (i.e Québec Covid Vaccination QR Codes)
som-tsp
Solving the Traveling Salesman Problem using Self-Organizing Maps
sports_betting_with_reinforcement_learning
This is the code for "Sports Betting with Reinforcement Learning" By Siraj Raval on Youtube
Stock-Trading-Environment
A custom OpenAI gym environment for simulating stock trades on historical price data.
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.
system-design-primer
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
tensorflow
Computation using data flow graphs for scalable machine learning
TensorFlow-Tutorials
TensorFlow Tutorials with YouTube Videos
ud120-projects
Starter project code for students taking Udacity ud120
zio-workshop
Real World Functional Programming with ZIO
zippers
A talk on zippers