Regression using LLMs alternatives and similar packages

Based on the "Machine Learning" category.
Alternatively, view llm-regression alternatives based on common mentions on social networks and blogs.

• tensorflow

  10.0 10.0 L1 Regression using LLMs VS tensorflow

  An Open Source Machine Learning Framework for Everyone
  

• Keras

  9.9 9.9 L2 Regression using LLMs VS Keras

  Deep Learning for humans
  

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• scikit-learn

  9.9 9.9 L3 Regression using LLMs VS scikit-learn

  scikit-learn: machine learning in Python
  

• gym

  9.8 0.0 Regression using LLMs VS gym

  A toolkit for developing and comparing reinforcement learning algorithms.
  

• xgboost

  9.8 9.6 L1 Regression using LLMs VS xgboost

  Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
  

• CNTK

  9.6 0.0 L1 Regression using LLMs VS CNTK

  Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
  

• PaddlePaddle

  9.6 10.0 L1 Regression using LLMs VS PaddlePaddle

  PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）
  

• Prophet

  9.5 7.0 Regression using LLMs VS Prophet

  Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
  

• MLflow

  9.5 9.9 Regression using LLMs VS MLflow

  Open source platform for the machine learning lifecycle
  

• gensim

  9.5 7.0 L3 Regression using LLMs VS gensim

  Topic Modelling for Humans
  

• MindsDB

  9.5 10.0 Regression using LLMs VS MindsDB

  The platform for customizing AI from enterprise data
  

• dspy

  9.1 9.9 Regression using LLMs VS dspy

  DSPy: The framework for programming—not prompting—foundation models
  

• TFLearn

  9.1 0.0 L3 Regression using LLMs VS TFLearn

  Deep learning library featuring a higher-level API for TensorFlow.
  

• H2O

  8.8 9.6 Regression using LLMs VS H2O

  H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
  

• NuPIC

  8.8 0.0 L3 Regression using LLMs VS NuPIC

  DISCONTINUED. Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex.
  

• Pyro.ai

  8.7 8.4 Regression using LLMs VS Pyro.ai

  Deep universal probabilistic programming with Python and PyTorch
  

• srez

  8.3 0.0 L5 Regression using LLMs VS srez

  DISCONTINUED. Image super-resolution through deep learning

• Surprise

  8.3 5.0 L4 Regression using LLMs VS Surprise

  A Python scikit for building and analyzing recommender systems

• LightFM

  7.9 0.0 L4 Regression using LLMs VS LightFM

  A Python implementation of LightFM, a hybrid recommendation algorithm.
  

• Pylearn2

  7.8 0.0 L2 Regression using LLMs VS Pylearn2

  Warning: This project does not have any current developer. See bellow.
  

• skflow

  7.6 1.3 L4 Regression using LLMs VS skflow

  DISCONTINUED. Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning
  

• Sacred

  7.5 3.5 Regression using LLMs VS Sacred

  Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
  

• PyBrain

  7.5 0.0 L4 Regression using LLMs VS PyBrain

  Another Python Machine Learning Library.

• Clairvoyant

  7.2 0.0 L3 Regression using LLMs VS Clairvoyant

  Software designed to identify and monitor social/historical cues for short term stock movement

• Metrics

  6.2 0.0 Regression using LLMs VS Metrics

  Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave

• python-recsys

  6.2 0.0 L4 Regression using LLMs VS python-recsys

  A python library for implementing a recommender system

• karateclub

  6.1 7.0 Regression using LLMs VS karateclub

  Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

• awesome-embedding-models

  6.0 0.0 Regression using LLMs VS awesome-embedding-models

  A curated list of awesome embedding models tutorials, projects and communities.

• pydeep

  5.9 0.0 L3 Regression using LLMs VS pydeep

  Deep learning in Python

• Crab

  5.7 0.0 L2 Regression using LLMs VS Crab

  Crab is a flexible, fast recommender engine for Python that integrates classic information filtering recommendation algorithms in the world of scientific Python packages (numpy, scipy, matplotlib).
  

• hebel

  5.0 0.0 L2 Regression using LLMs VS hebel

  GPU-Accelerated Deep Learning Library in Python

• seqeval

  4.6 0.0 Regression using LLMs VS seqeval

  A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)
  

• adaptive

  4.5 6.2 Regression using LLMs VS adaptive

  :chart_with_upwards_trend: Adaptive: parallel active learning of mathematical functions
  

• Xorbits

  4.4 8.0 Regression using LLMs VS Xorbits

  Scalable Python DS & ML, in an API compatible & lightning fast way.
  

• TrueSkill, the video game rating system

  4.2 1.4 Regression using LLMs VS TrueSkill, the video game rating system

  An implementation of the TrueSkill rating system for Python

• SciKit-Learn Laboratory

  3.9 8.7 Regression using LLMs VS SciKit-Learn Laboratory

  SciKit-Learn Laboratory (SKLL) makes it easy to run machine learning experiments.
  

• pdpipe

  3.9 0.0 Regression using LLMs VS pdpipe

  Easy pipelines for pandas DataFrames.
  

• rwa

  3.8 0.0 L5 Regression using LLMs VS rwa

  Machine Learning on Sequential Data Using a Recurrent Weighted Average

• Feature Forge

  3.5 0.0 L4 Regression using LLMs VS Feature Forge

  A set of tools for creating and testing machine learning features, with a scikit-learn compatible API
  

• nptyping

  3.4 0.0 Regression using LLMs VS nptyping

  💡 Type hints for Numpy and Pandas

• Data Flow Facilitator for Machine Learning (dffml)

  3.3 9.0 Regression using LLMs VS Data Flow Facilitator for Machine Learning (dffml)

  The easiest way to use Machine Learning. Mix and match underlying ML libraries and data set sources. Generate new datasets or modify existing ones with ease.
  

• brew

  3.2 0.0 L4 Regression using LLMs VS brew

  DISCONTINUED. Multiple Classifier Systems and Ensemble Learning Library in Python.

• Robocorp Action Server

  3.2 9.8 Regression using LLMs VS Robocorp Action Server

  Create 🐍 Python AI Actions and 🤖 Automations, and deploy & operate them anywhere
  

• bodywork

  3.1 0.0 Regression using LLMs VS bodywork

  DISCONTINUED. ML pipeline orchestration and model deployments on Kubernetes.
  

• MLP Classifier

  2.8 0.0 L4 Regression using LLMs VS MLP Classifier

  A handwritten multilayer perceptron classifer using numpy.

• OptaPy

  2.7 5.5 Regression using LLMs VS OptaPy

  OptaPy is an AI constraint solver for Python to optimize planning and scheduling problems.
  

• redframes

  2.7 1.4 Regression using LLMs VS redframes

  General Purpose Data Manipulation Library

• openskill.py

  2.6 7.0 Regression using LLMs VS openskill.py

  Multiplayer Rating System. No Friction.
  

• vowpal_porpoise

  2.5 0.0 L3 Regression using LLMs VS vowpal_porpoise

  lightweight python wrapper for vowpal wabbit

• omega-ml

  1.8 8.1 Regression using LLMs VS omega-ml

  MLOps simplified. From ML Pipeline ⇨ Data Product without the hassle

* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.

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Popular Comparisons

• Keras
  vs
  scikit-learn
• tensorflow
  vs
  PaddlePaddle
• Surprise
  vs
  LightFM
• tensorflow
  vs
  scikit-learn
• PyBrain
  vs
  tensorflow

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