DSA VS Deep_Learning_Machine_Learning_Stock

Compare DSA vs Deep_Learning_Machine_Learning_Stock and see what are their differences.

DSA

This repository contains useful algorithms and data structures implemented in the simplest way as much as we can. (by venkateshpensalwar)
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DSA Deep_Learning_Machine_Learning_Stock
1 48
3 1,149
- -
0.0 6.1
almost 3 years ago 2 months ago
Jupyter Notebook Jupyter Notebook
- MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

DSA

Posts with mentions or reviews of DSA. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing DSA and Deep_Learning_Machine_Learning_Stock you can also consider the following projects:

Crack-Your-Placement - 🎯 Repo for placement-preparation, DSA, Problem-Solving, Competitive Programming, CS-Fundamentals

tsfresh - Automatic extraction of relevant features from time series:

linkedlist - The first generic linked list in go :dancer:

bulbea - :boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling

FinanceDataReader - Financial data reader [Moved to: https://github.com/FinanceData/FinanceDataReader]

TradingGym - Trading Gym is an open source project for the development of reinforcement learning algorithms in the context of trading.

deltapy - DeltaPy - Tabular Data Augmentation (by @firmai)

DataScienceProjects

ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.

Algo-Trading - This is my github repository where I post trading strategies, tutorials and research on quantitative finance with R, C++ and Python. Some of the topics explored include: machine learning, high frequency trading, NLP, technical analysis and more. Hope you enjoy it!

Astock - Astock

machine-learning-for-trading - Code for Machine Learning for Algorithmic Trading, 2nd edition.