Neural Networks: Zero to Hero
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The simplest, fastest repository for training/finetuning medium-sized GPTs.
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Prompt engineering is a relatively new discipline for developing and optimizing prompts to efficiently use language models (LMs) for a wide variety of applications and research topics.
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notebooks for pawarbi.github.io/blog/
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MachineLearningStocks is designed to be an intuitive and highly extensible template project applying machine learning to making stock predictions. Source from: https://github.com/robertmartin8/MachineLearningStocks Caution!!! As of Feb 2021: MachineLearningStocks is no longer actively maintained
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A summary from a Chinese author "bitcarmanlee". Chinese contents. Please go to the author's link https://github.com/bitcarmanlee/easy-algorithm-interview-and-practice
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Set of Jupyter notebook supporting articles on https://medium.com/@vdekanovsky
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HTTP(S) benchmark tools, testing/debugging, & restAPI (RESTful)
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A collection of resources for pandas (Python) and related subjects.
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It is an utility library for Kaggle and offline competitions. It is particularly focused on experiment tracking, feature engineering, and validation.
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This repository contains the entire Python Data Science Handbook, in the form of (free!) Jupyter notebooks. https://jakevdp.github.io/PythonDataScienceHandbook/
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Jupylet is a Python library for programming 2D and 3D games, graphics, music and sound synthesizers, interactively in a Jupyter notebook. It is intended for three types of audiences: Computer scientists, researchers, and students of deep reinforcement learning. Musicians interested in sound synthesis and live music coding. Kids and their parents interested in learning to program.
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sql magic for IPython, hopefully evolving into full SQL client. http://catherinedevlin.blogspot.com/
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code and data for the time series analysis video from YouTube channel: https://www.youtube.com/@ritvikmath
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Sample and simple code sharing for Capital Budgeting with Monte Carlo Simulation
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An Ultimate Compilation of AI Resources for Mathematics, Machine Learning and Deep Learning. Highly recommended!
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It is a Technical Analysis library useful to do feature engineering from financial time series datasets (Open, Close, High, Low, Volume). It is built on Pandas and Numpy. Project url: https://technical-analysis-library-in-python.readthedocs.io/en/latest/
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Jupyter notebooks from the scikit-learn video series Youtube link: https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A
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ML for Trading - 2nd Edition This book aims to show how ML can add value to algorithmic trading strategies in a practical yet comprehensive way. It covers a broad range of ML techniques from linear regression to deep reinforcement learning and demonstrates how to build, backtest, and evaluate a trading strategy driven by model predictions.
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IPython notebooks with demo code intended as a companion to the book: Data Driven Science & Engineering: Machine Learning, Dynamical Systems, and Control by S. L. Brunton and J. N. Kutz http://databookuw.com/
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