Search Results for "math with python learn" - Page 2
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Showing 474 open source projects for "math with python learn"
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Dask
Parallel computing with task scheduling
Dask is a Python library for parallel and distributed computing, designed to scale analytics workloads from single machines to large clusters. It integrates with familiar tools like NumPy, Pandas, and scikit-learn while enabling execution across cores or nodes with minimal code changes. Dask excels at handling large datasets that don’t fit into memory and is widely used in data science, machine learning, and big data pipelines. -
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LTX-2
Python inference and LoRA trainer package for the LTX-2 audio–video
LTX-2 is a powerful, open-source toolkit developed by Lightricks that provides a modular, high-performance base for building real-time graphics and visual effects applications. It is architected to give developers low-level control over rendering pipelines, GPU resource management, shader orchestration, and cross-platform abstractions so they can craft visually compelling experiences without starting from scratch. Beyond basic rendering scaffolding, LTX-2 includes optimized math libraries,... -
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Baby Buddy
Helps caregivers track sleep, feedings, diaper changes, and tummy time
A buddy for babies! Helps caregivers track sleep, feedings, diaper changes, tummy time and more to learn about and predict baby's needs without (as much) guesswork. A buddy to help caregivers track sleep, feedings, diaper changes, and tummy time to learn about and predict baby's needs without (as much) guess work. A demo of Baby Buddy is available on Heroku. The demo instance resets every hour. Baby Buddy is available in a variety of languages thanks to the efforts of numerous translators.... -
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TPOT
A Python Automated Machine Learning tool that optimizes ML
Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. -
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SymPy
A computer algebra system written in pure Python
SymPy is an open source Python library for symbolic mathematics. Its goal is to become a full-featured computer algebra system (CAS) while maintaining the simplicity of its code. Written entirely in Python, SymPy is easy to use, comprehensible and easily extensible. It’s also very lightweight as it solely depends on mpmath, a pure Python library for arbitrary floating point arithmetic. SymPy has participated in every Google Summer of Code since 2007 and because of this has continuously... -
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TabFM
scikit-learn compatible tabular foundation model
...Instead of fitting model weights to the user’s dataset, TabFM uses in-context learning by reading training examples and test rows together at inference time. The library provides scikit-learn-compatible classifier and regressor interfaces, which makes it familiar for data scientists already using Python ML workflows. It supports both JAX and PyTorch backends and can automatically download pretrained TabFM v1.0.0 weights. The project is useful for practitioners who want strong tabular predictions with less manual feature engineering, tuning, and repeated model training. -
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ML for Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
...The repository includes quizzes, solutions, and instructor materials to make the content usable in classrooms or self-study. It emphasizes ethical considerations and model evaluation—accuracy is not the only metric—so students learn to validate and communicate results responsibly. By the end, participants can build end-to-end ML experiments, interpret outputs, and iterate with confidence rather than just copying code. -
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NumPy
The fundamental package for scientific computing with Python
...NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. -
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Typer
Typer, build great CLIs, based on Python type hints
Typer is a library for building CLI applications that users will love using and developers will love creating. Based on Python 3.6+ type hints. Great editor support. Completion everywhere. Less time debugging. Designed to be easy to use and learn. Less time reading docs. It's easy to use for the final users. Automatic help, and automatic completion for all shells. Minimize code duplication. Multiple features from each parameter declaration. Fewer bugs. The simplest example adds only 2 lines of code to your app: 1 import, 1 function call. ... -
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Amazon CodeGuru Profiler Python Agent
Amazon CodeGuru Profiler Python Agent
Amazon CodeGuru Profiler collects runtime performance data from your live applications and provides recommendations that can help you fine-tune your application performance. Using machine learning algorithms, CodeGuru Profiler can help you find your most expensive lines of code and suggest ways you can improve efficiency and remove CPU bottlenecks. CodeGuru Profiler provides different visualizations of profiling data to help you identify what code is running on the CPU, see how much time is... -
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Book3_Elements-of-Mathematics
From Addition, Subtraction, Multiplication, and Division to ML
Book3_Elements-of-Mathematics is an open learning resource in the Visualize-ML collection that introduces core mathematical foundations required for modern data science and AI. The repository presents topics such as algebra, calculus fundamentals, and mathematical reasoning using a highly visual and beginner-friendly approach. Its goal is to reduce the intimidation barrier often associated with formal mathematics by combining diagrams, structured explanations, and applied examples. The... -
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Zipline Reloaded
Zipline, a Pythonic Algorithmic Trading Library
...Pandas-based input and output integrate naturally with the broader Python data-science ecosystem. Strategies can also use libraries such as SciPy, Matplotlib, statsmodels, and scikit-learn for analysis and modeling. The maintained fork updates dependencies and compatibility so the established Zipline workflow remains usable on modern Python environments. -
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Bayesian Optimization
Python implementation of global optimization with gaussian processes
This is a constrained global optimization package built upon bayesian inference and gaussian process, that attempts to find the maximum value of an unknown function in as few iterations as possible. This technique is particularly suited for optimization of high cost functions, situations where the balance between exploration and exploitation is important. More detailed information, other advanced features, and tips on usage/implementation can be found in the examples folder. Follow the basic... -
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peewee
A small, expressive orm, which supports postgresql, mysql and sqlite
Peewee is a simple and small ORM. It has few (but expressive) concepts, making it easy to learn and intuitive to use. Peewee will automatically infer the database table name from the name of the class. You can override the default name by specifying a table_name attribute in the inner “Meta” class (alongside the database attribute). To learn more about how Peewee generates table names, refer to the Table Names section. There are lots of field types suitable for storing various types of data. ... -
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YOLOv9
Learning What You Want to Learn Using Programmable Gradient Info
YOLOv9 is the official implementation of the paper “YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information.” It is a modern object detection repository focused on improving how deep networks preserve useful information during training. The project introduces Programmable Gradient Information and the GELAN architecture to improve gradient flow, parameter efficiency, and train-from-scratch performance. It provides scripts and model assets for training, testing, and... -
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DreamerV3
Mastering Diverse Domains through World Models
DreamerV3 is an open-source implementation of a reinforcement learning algorithm that uses world models to train intelligent agents capable of learning complex behaviors across many environments. The system works by building an internal model of the environment and then using that model to simulate possible future outcomes of actions, allowing the agent to learn from imagined experiences rather than only from real interactions. This approach enables the algorithm to efficiently learn... -
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SQLModel
SQL databases in Python, designed for simplicity, compatibility
SQLModel, SQL databases in Python, designed for simplicity, compatibility, and robustness. SQLModel is a library for interacting with SQL databases from Python code, with Python objects. It is designed to be intuitive, easy to use, highly compatible, and robust. SQLModel is based on Python-type annotations, and powered by Pydantic and SQLAlchemy. -
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openbench
Provider-agnostic, open-source evaluation infrastructure
openbench is an open-source, provider-agnostic evaluation infrastructure designed to run standardized, reproducible benchmarks on large language models (LLMs), enabling fair comparison across different model providers. It bundles dozens of evaluation suites — covering knowledge, reasoning, math, code, science, reading comprehension, long-context recall, graph reasoning, and more — so users don’t need to assemble disparate datasets themselves. With a simple CLI interface (e.g. bench eval... -
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Feature-engine
Feature engineering package with sklearn like functionality
Feature-engine is a Python library with multiple transformers to engineer and select features for use in machine learning models. Feature-engine's transformers follow Scikit-learn's functionality with fit() and transform() methods to learn the transforming parameters from the data and then transform it. -
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Scikit-LLM
Seamlessly integrate LLMs into scikit-learn
Seamlessly integrate powerful language models like ChatGPT into sci-kit-learn for enhanced text analysis tasks. At the moment the majority of the Scikit-LLM estimators are only compatible with some of the OpenAI models. Hence, a user-provided OpenAI API key is required. Additionally, Scikit-LLM will ensure that the obtained response contains a valid label. If this is not the case, a label will be selected randomly (label probabilities are proportional to label occurrences in the training... -
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Tencent-Hunyuan-Large
Open-source large language model family from Tencent Hunyuan
Tencent-Hunyuan-Large is the flagship open-source large language model family from Tencent Hunyuan, offering both pre-trained and instruct (fine-tuned) variants. It is designed with long-context capabilities, quantization support, and high performance on benchmarks across general reasoning, mathematics, language understanding, and Chinese / multilingual tasks. It aims to provide competitive capability with efficient deployment and inference. FP8 quantization support to reduce memory usage... -
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NGBoost
Natural Gradient Boosting for Probabilistic Prediction
ngboost is a Python library that implements Natural Gradient Boosting, as described in "NGBoost: Natural Gradient Boosting for Probabilistic Prediction". It is built on top of Scikit-Learn and is designed to be scalable and modular with respect to the choice of proper scoring rule, distribution, and base learner. A didactic introduction to the methodology underlying NGBoost is available in this slide deck. -
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Astropy
Repository for the Astropy core package
The Astropy Project is a community effort to develop a common core package for Astronomy in Python and foster an ecosystem of interoperable astronomy packages. Astropy is a Python library for use in astronomy. Learn Astropy provides a portal to all of the Astropy educational material through a single dynamically searchable web page. It allows you to filter tutorials by keywords, search for filters, and make search queries in tutorials and documentation simultaneously. ... -
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Theseus
A library for differentiable nonlinear optimization
Theseus is a library for differentiable nonlinear optimization that lets you embed solvers like Gauss-Newton or Levenberg–Marquardt inside PyTorch models. Problems are expressed as factor graphs with variables on manifolds (e.g., SE(3), SO(3)), so classical robotics and vision tasks—bundle adjustment, pose graph optimization, hand–eye calibration—can be written succinctly and solved efficiently. Because solves are differentiable, you can backpropagate through optimization to learn cost... -
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System Design Primer
Learn how to design large-scale systems
System Design Primer is a curated, open source collection of resources that helps engineers learn how to design large-scale systems. The project is structured as a comprehensive guide covering core system design concepts, trade-offs, and patterns necessary for building scalable, reliable, and maintainable systems. It offers both theoretical foundations—such as scalability principles, the CAP theorem, and consistency models—and practical exercises, including real-world system design interview...
