Starred repositories
A Comprehensive Collection of Everything Related to Tradingview Pine Script.
Script to submit a buy order followed by an automatic sell OCO order
Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.
🔎 📈 🐍 💰 Backtest trading strategies in Python.
Find big moving stocks before they move using machine learning and anomaly detection
Use unsupervised and supervised learning to predict stocks
Algorithmic trading and quantitative trading open source platform to develop trading robots (stock markets, forex, crypto, bitcoins, and options).
This is a repository containing the list of company wise questions available on leetcode premium
Crack LeetCode, not only how, but also why.
Roadmap to becoming a data engineer in 2021
⭐️ Companies that don't have a broken hiring process
🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
Concise, consistent, and legible badges in SVG and raster format
Learn to Code While Building Apps - The Complete Flutter Development Bootcamp
A curated list of awesome Flask resources and plugins
Learn how to develop, deploy and iterate on production-grade ML applications.
machine learning and deep learning tutorials, articles and other resources
📊 Path to a free self-taught education in Data Science!
✅ Curated list of resources for developers
Reference implementations of popular deep learning models.
Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course
Tutorials, assignments, and competitions for MIT Deep Learning related courses.
A Deep Learning based project for colorizing and restoring old images (and video!)
深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06
🏭 Collaboratively build, visualize, and design neural nets in browser
A curated list of awesome warez and piracy links
📡 Simple and ready-to-use tutorials for TensorFlow