A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
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Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
A curated list of awesome libraries, packages, strategies, books, blogs, tutorials for systematic trading.
Open Machine Learning Course
A sample project that exists for PyPUG's "Tutorial on Packaging and Distributing Projects"
LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data
📦 A Human's Ultimate Guide to setup.py.
- python
- guide
- setuptools
- distutils
TranslatedThis is a step-by-step guide to getting started with AI/LLM large models, including tutorials and demo code, taking you from APIs to local large model deployment and fine-tuning. The code files come with Kaggle or Colab online versions, so you can learn even without a graphics card. The project also has a small code playground 🎡 where you can try out some interesting AI scripts. It also includes complete Chinese mirror assignments for Hung-Yi Lee's (HUNG-YI LEE) 2024 Introduction to Generative AI course.
Synapsa Commons: free, hands-on AI courses that run anywhere (Colab, Kaggle, Binder, Codespaces, Jupyter). EU AI Act conformity evidence, model validation, predictive maintenance, document extraction, retrieval and RAG, AI agents and MCP, a simulated humanoid. Every lesson autograded. From the team building Synapsa.
A structured, multi-agent Claude Code learning environment for mastering Retrieval-Augmented Generation (RAG)