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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]

book-python-for-finance

Jupyter Notebooks and codes for Python for Finance (2nd ed., O'Reilly) by Yves Hilpisch.

data-science--cheat-sheet

Cheat Sheets

deep-learning-for-hackers

Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)

deep-rl-class

This repo contains the syllabus of the Hugging Face Deep Reinforcement Learning Course.

equity-portfolio-prediction

geron_handson-ml3

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

grokking-deep-learning

this repository accompanies my forthcoming book "Grokking Deep Learning"

how-to-win-a-data-science-competition

How to Win a Data Science Competition: Learn from Top Kagglers

indicators

Collection of indicators that I used in my strategies.

machine-learning-for-algorithmic-trading-second-edition

Code and resources for Machine Learning for Algorithmic Trading, 2nd edition.

maintenance-in-manufacturing-systemsmaintsim

Simulation of maintenance in manufacturing systems

mastering-python-for-finance-second-edition

Mastering Python for Finance – Second Edition, published by Packt

multi-echelon-inventory-optimization

multi-echelon inventory optimization with SimPy, SciPy, sklearn, and RBFOpt

predictive-maintenance

Data Wrangling, EDA, Feature Engineering, Model Selection, Regression, Binary and Multi-class Classification (Python, scikit-learn)

quantopian-research_public

Quantitative research and educational materials

quantra_ml-trading-book

This repository contains the python codes as well as data files which have been included in the ML for Trading ebook

retailreplenishement

CRP Code

stock_screener

Picking stocks through various screening methods. Focus on Northern Europe.

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.

supply-chain-and-logistics-analytics

supply_chain_analytics

symon_dlcourse_ai

Материалы курса Deep Learning на пальцах

technical_analysis_and_feature_engineering

Feature Engineering and Feature Importance of Machine Learning in Financial Market.

tensorflow-tutorials

TensorFlow Tutorials with YouTube Videos

thinkdigitalsignakp

Think DSP: Digital Signal Processing in Python, by Allen B. Downey.

yndx-mashinnoye-obucheniye

:books: Специализация «Машинное обучение и анализ данных»