Search Results for "syntax tree model"
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Showing 23 open source projects for "syntax tree model"
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Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI Studio. Switch between models without switching platforms.
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CodePilot
A native desktop GUI for Claude Code
CodePilot is a native desktop graphical user interface built for Claude Code that lets developers chat with, code with, and manage AI-assisted projects visually rather than through the terminal. Created with Electron and Next.js, CodePilot delivers a polished experience where users can talk to Claude models, view syntax-highlighted responses, attach files, and inspect project context via a live file tree. It supports session management so chats and project work persist between restarts, letting users pick up where they left off without losing history. Unlike traditional CLI-only workflows, CodePilot brings panels, drag-to-resize layouts, and controls for tool permissions that make it feel like a modern desktop code assistant. ... -
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dtreeviz
Python library for decision tree visualization & model interpretation
A python library for decision tree visualization and model interpretation. Decision trees are the fundamental building block of gradient boosting machines and Random Forests(tm), probably the two most popular machine learning models for structured data. Visualizing decision trees is a tremendous aid when learning how these models work and when interpreting models. -
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talos
Hyperparameter Optimization for TensorFlow, Keras and PyTorch
Talos radically changes the ordinary Keras, TensorFlow (tf.keras), and PyTorch workflow by fully automating hyperparameter tuning and model evaluation. Talos exposes Keras and TensorFlow (tf.keras) and PyTorch functionality entirely and there is no new syntax or templates to learn. Talos is made for data scientists and data engineers that want to remain in complete control of their TensorFlow (tf.keras) and PyTorch models, but are tired of mindless parameter hopping and confusing optimization solutions that add complexity instead of reducing it. ... -
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SHAP
A game theoretic approach to explain the output of ml models
SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions. While SHAP can explain the output of any machine learning model, we have developed a high-speed exact algorithm for tree ensemble methods. Fast C++ implementations are supported for XGBoost, LightGBM, CatBoost, scikit-learn and pyspark tree models. ... -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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TUUI
A desktop MCP client designed as a tool unitary utility integration
...Many components within the project have been directly converted or generated from the prototype project through AI. Given the considerations regarding the quality and safety of AI-generated content, this project employs strict syntax checks and naming conventions. Therefore, for any further development, please ensure that you use the linting tools I've set up to check and automatically fix syntax issues. -
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LightGBM
Gradient boosting framework based on decision tree algorithms
LightGBM or Light Gradient Boosting Machine is a high-performance, open source gradient boosting framework based on decision tree algorithms. Compared to other boosting frameworks, LightGBM offers several advantages in terms of speed, efficiency and accuracy. Parallel experiments have shown that LightGBM can attain linear speed-up through multiple machines for training in specific settings, all while consuming less memory. LightGBM supports parallel and GPU learning, and can handle... -
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KerasTuner
A Hyperparameter Tuning Library for Keras
KerasTuner is an easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search. Easily configure your search space with a define-by-run syntax, then leverage one of the available search algorithms to find the best hyperparameter values for your models. KerasTuner comes with Bayesian Optimization, Hyperband, and Random Search algorithms built-in, and is also designed to be easy for researchers to extend in order to experiment with new search... -
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spaCy
Industrial-strength Natural Language Processing (NLP)
spaCy is a library built on the very latest research for advanced Natural Language Processing (NLP) in Python and Cython. Since its inception it was designed to be used for real world applications-- for building real products and gathering real insights. It comes with pretrained statistical models and word vectors, convolutional neural network models, easy deep learning integration and so much more. spaCy is the fastest syntactic parser in the world according to independent benchmarks, with... -
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Kimi k1.5
Scaling Reinforcement Learning with LLMs
...By using techniques like partial rollouts to improve training efficiency and applying sophisticated policy optimization methods, the developers demonstrate that strong ability can emerge without relying on complex solutions like Monte Carlo tree search or value functions. Kimi-k1.5 is trained jointly on text and vision data, giving it true multimodal reasoning capabilities where it can interpret and generate content across modalities in a unified way. -
Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
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FATE
An industrial grade federated learning framework
FATE (Federated AI Technology Enabler) is the world's first industrial grade federated learning open source framework to enable enterprises and institutions to collaborate on data while protecting data security and privacy. It implements secure computation protocols based on homomorphic encryption and multi-party computation (MPC). Supporting various federated learning scenarios, FATE now provides a host of federated learning algorithms, including logistic regression, tree-based algorithms,... -
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TraceRoot
Find the Root Cause in Your Code's Trace
TraceRoot.AI is an open source, AI-native observability and debugging platform designed to help engineering teams resolve production issues faster. It consolidates telemetry into a single correlated execution tree that provides causal context for failures. AI agents operate over this structured view to summarize issues, pinpoint likely root causes, and even suggest actionable fixes or draft GitHub issues and pull requests. It offers interactive trace exploration with zoomable log clusters,... -
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NeuralForecast
Scalable and user friendly neural forecasting algorithms.
NeuralForecast offers a large collection of neural forecasting models focusing on their performance, usability, and robustness. The models range from classic networks like RNNs to the latest transformers: MLP, LSTM, GRU, RNN, TCN, TimesNet, BiTCN, DeepAR, NBEATS, NBEATSx, NHITS, TiDE, DeepNPTS, TSMixer, TSMixerx, MLPMultivariate, DLinear, NLinear, TFT, Informer, AutoFormer, FedFormer, PatchTST, iTransformer, StemGNN, and TimeLLM. There is a shared belief in Neural forecasting methods'... -
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files-to-prompt
Concatenate a directory full of files into a single prompt
files-to-prompt is a Python command-line tool that takes one or more files or entire directories and concatenates their contents into a single, LLM-friendly prompt. It walks the directory tree, outputting each file preceded by its relative path and a separator, so a model can understand which content came from where. The tool is aimed at workflows where you want to ask an LLM questions about a whole codebase, documentation set, or notes folder without manually copying files together. It includes rich filtering controls, letting you limit by extension, include or skip hidden files, and ignore paths that match glob patterns or .gitignore rules. ... -
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Repo of Tree of Thoughts (ToT)
Implementation of "Tree of Thoughts
...This means they can fall short in tasks that require exploration, strategic lookahead, or where initial decisions play a pivotal role. To surmount these challenges, we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of Thought approach to prompting language models and enables exploration over coherent units of text (thoughts) that serve as intermediate steps toward problem-solving. ToT allows LMs to perform deliberate decision-making by considering multiple different reasoning paths and self-evaluating choices to decide the next course of action, as well as looking ahead or backtracking when necessary to make global choices. -
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OpenAI Web Application
A web application that allows users to interact with OpenAI's models
...Utilize Whisper Model to transcribe audio into text. -
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Merlion
A Machine Learning Framework for Time Series Intelligence
Merlion is a Python library for time series intelligence. It provides an end-to-end machine learning framework that includes loading and transforming data, building and training models, post-processing model outputs, and evaluating model performance. It supports various time series learning tasks, including forecasting, anomaly detection, and change point detection for both univariate and multivariate time series. This library aims to provide engineers and researchers a one-stop solution to... -
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ModelFox
ModelFox makes it easy to train, deploy, and monitor ML models
...Train a machine learning model by running modelfox train with the path to a CSV file and the name of the column you want to predict. The CLI automatically transforms your data into features, trains a number of linear and gradient boosted decision tree models to predict the target column, and writes the best model to a .modelfox file. If you want more control, you can provide a config file. -
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Forecasting Best Practices
Time Series Forecasting Best Practices & Examples
Time series forecasting is one of the most important topics in data science. Almost every business needs to predict the future in order to make better decisions and allocate resources more effectively. This repository provides examples and best practice guidelines for building forecasting solutions. The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in forecasting algorithms to build solutions and operationalize them. Rather than... -
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DarkForestGo
DarkForest, the Facebook Go engine
darkforestGo is an early deep-reinforcement-learning Go engine that combined a convolutional policy/value network with Monte Carlo Tree Search (MCTS) to play the full 19×19 game at a strong amateur level. The system couples fast GPU policy inference with CPU or GPU-assisted tree search so priors from the network guide exploration while search refines local tactics. Training pipelines mix supervised learning from human professional games and self-play fine-tuning, allowing the model to learn opening patterns and endgame tactics beyond simple pattern libraries. ... -
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popt4jlib
Parallel Optimization Library for Java
popt4jlib is an open-source parallel optimization library for the Java programming language supporting both shared memory and distributed message passing models. Implements a number of meta-heuristic algorithms for Non-Linear Programming, including Genetic Algorithms, Differential Evolution, Evolutionary Algorithms, Simulated Annealing, Particle Swarm Optimization, Firefly Algorithm, Monte-Carlo Search, Local Search algorithms, Gradient-Descent-based algorithms, as well as some well-known... -
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TextBlob
TextBlob is a Python library for processing textual data
Simple, Pythonic, text processing, Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more. TextBlob stands on the giant shoulders of NLTK and pattern, and plays nicely with both. Supports word inflection (pluralization and singularization) and lemmatization,... -
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Open BEAGLE
Evolutionary Computation Framework in C++
Open BEAGLE is a C++ Evolutionary Computation (EC) framework. It provides an high-level software environment to do any kind of EC, with support for tree-based genetic programming; bit string, integer-valued vector, and real-valued vector genetic algorithms; and evolution strategy. The Open BEAGLE architecture follows strong principles of object oriented programming, where abstractions are represented by loosely coupled objects and where it is common and easy to reuse code. Open BEAGLE is... -
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Matlab Classification Toolbox contains implementations of the following classifiers: Naive Bayes, Gaussian, Gaussian Mixture Model, Decision Tree and Neural Networks. This toolbox allows users to compare classifiers across various data sets.
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