Search Results for "python-kinterbasdb" - Page 10
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Showing 664 open source projects for "python-kinterbasdb"
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SoftCo: Enterprise Invoice and P2P Automation SoftwareSoftCo Accounts Payable Automation processes all PO and non-PO supplier invoices electronically from capture and matching through to invoice approval and query management. SoftCoAP delivers unparalleled touchless automation by embedding AI across matching, coding, routing, and exception handling to minimize the number of supplier invoices requiring manual intervention. The result is 89% processing savings, supported by a context-aware AI Assistant that helps users understand exceptions, answer questions, and take the right action faster.
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BitNet
BitNet: Scaling 1-bit Transformers for Large Language Models
BitNet is a machine learning research implementation that explores extremely low-precision neural network architectures designed to dramatically reduce the computational cost of large language models. The project implements the BitNet architecture described in research on scaling transformer models using extremely low-bit quantization techniques. In this approach, neural network weights are quantized to approximately one bit per parameter, allowing models to operate with far lower memory... -
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HivisionIDPhoto
HivisionIDPhotos: a lightweight and efficient AI ID photos tools
HivisionIDPhotos is an open-source AI project designed to automatically generate professional ID photographs from ordinary portrait images. The system uses computer vision and machine learning models to detect faces, segment the subject from the background, and produce standardized identification photos suitable for official documents. It is designed as a lightweight tool that can perform inference offline and run efficiently on CPUs without requiring powerful GPUs. The software analyzes... -
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DeepVariant
DeepVariant is an analysis pipeline that uses a deep neural networks
DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data. DeepVariant is a deep learning-based variant caller that takes aligned reads (in BAM or CRAM format), produces pileup image tensors from them, classifies each tensor using a convolutional neural network, and finally reports the results in a standard VCF or gVCF file. DeepTrio is a deep learning-based trio variant caller built on top of DeepVariant. DeepTrio... -
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PyG
Graph Neural Network Library for PyTorch
PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support,... -
The full-stack observability platform that protects your dataLayer, tags and conversion dataCode-Cube.io detects issues instantly, alerts you in real time and helps you resolve them fast. No manual QA. No unreliable data. Just data you can trust and act on.
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TensorFlow Model Garden
Models and examples built with TensorFlow
The TensorFlow Model Garden is a repository with a number of different implementations of state-of-the-art (SOTA) models and modeling solutions for TensorFlow users. We aim to demonstrate the best practices for modeling so that TensorFlow users can take full advantage of TensorFlow for their research and product development. To improve the transparency and reproducibility of our models, training logs on TensorBoard.dev are also provided for models to the extent possible though not all models... -
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Raster Vision
Open source framework for deep learning satellite and aerial imagery
Raster Vision is an open source framework for Python developers building computer vision models on satellite, aerial, and other large imagery sets (including oblique drone imagery). There is built-in support for chip classification, object detection, and semantic segmentation using PyTorch. Raster Vision allows engineers to quickly and repeatably configure pipelines that go through core components of a machine learning workflow: analyzing training data, creating training chips, training models, creating predictions, evaluating models, and bundling the model files and configuration for easy deployment. ... -
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Albumentations
Fast image augmentation library and an easy-to-use wrapper
Albumentations is a computer vision tool that boosts the performance of deep convolutional neural networks. Albumentations is a Python library for fast and flexible image augmentations. Albumentations efficiently implements a rich variety of image transform operations that are optimized for performance, and does so while providing a concise, yet powerful image augmentation interface for different computer vision tasks, including object classification, segmentation, and detection. ... -
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mlforecast
Scalable machine learning for time series forecasting
mlforecast is a time-series forecasting framework built around machine-learning models, designed to make forecasting both efficient and scalable. It lets you apply any regressor that follows the typical scikit-learn API, for example, gradient-boosted trees or linear models, to time-series data by automating much of the messy feature engineering and data preparation. Instead of writing custom code to build lagged features, rolling statistics, and date-based predictors, mlforecast generates... -
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fastdup
An unsupervised and free tool for image and video dataset analysis
fastdup is a powerful free tool designed to rapidly extract valuable insights from your image & video datasets. Assisting you to increase your dataset images & labels quality and reduce your data operations costs at an unparalleled scale. -
Securden Privileged Account ManagerDiscover and manage administrator, service, and web app passwords, keys, and identities. Automate management with approval workflows. Centrally control, audit, monitor, and record all access to critical IT assets.
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Physical Symbolic Optimization (Φ-SO)
Physical Symbolic Optimization
Physical Symbolic Optimization (Φ-SO) - A symbolic optimization package built for physics. Symbolic regression module uses deep reinforcement learning to infer analytical physical laws that fit data points, searching in the space of functional forms. -
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Llama Recipes
Scripts for fine-tuning Meta Llama3 with composable FSDP & PEFT method
The 'llama-recipes' repository is a companion to the Meta Llama models. We support the latest version, Llama 3.1, in this repository. The goal is to provide a scalable library for fine-tuning Meta Llama models, along with some example scripts and notebooks to quickly get started with using the models in a variety of use-cases, including fine-tuning for domain adaptation and building LLM-based applications with Llama and other tools in the LLM ecosystem. The examples here showcase how to run... -
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AIF360
A comprehensive set of fairness metrics for datasets
...The AI Fairness 360 toolkit is an extensible open-source library containing techniques developed by the research community to help detect and mitigate bias in machine learning models throughout the AI application lifecycle. AI Fairness 360 package is available in both Python and R. The AI Fairness 360 interactive experience provides a gentle introduction to the concepts and capabilities. The tutorials and other notebooks offer a deeper, data scientist-oriented introduction. The complete API is also available. Being a comprehensive set of capabilities, it may be confusing to figure out which metrics and algorithms are most appropriate for a given use case. ... -
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RL with PyTorch
Clean, Robust, and Unified PyTorch implementation
RL with PyTorch is a research-oriented repository that provides implementations of deep reinforcement learning algorithms using the PyTorch framework. The project focuses on helping developers and researchers understand reinforcement learning methods by providing clean and reproducible implementations of well-known algorithms. It includes code for popular deep reinforcement learning techniques such as Deep Q-Networks, policy gradient methods, actor-critic architectures, and other modern RL... -
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Data Science Articles from CodeCut
Collection of useful data science topics along with articles
The Data-science repository from CodeCutTech is a curated collection of educational content focused on practical tools and workflows used in modern data science projects. Instead of providing a single software package, the repository aggregates articles, tutorials, and examples covering many topics within the data science ecosystem. The materials address areas such as MLOps, data management, project organization, testing practices, visualization techniques, and productivity tools used by... -
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ML-NLP
This project is a common knowledge point and code implementation
ML-NLP is a large open-source repository that collects theoretical knowledge, practical explanations, and code examples related to machine learning, deep learning, and natural language processing. The project is designed primarily as a learning resource for algorithm engineers and students preparing for technical interviews in machine learning or NLP roles. It compiles important concepts that frequently appear in machine learning discussions, including neural network architectures, training... -
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Karpathy
An agentic Machine Learning Engineer
karpathy is an experimental agentic machine learning engineer framework designed to automate many aspects of the ML development workflow. The project sets up a sandboxed environment where an AI agent can access datasets, run experiments, and generate machine learning artifacts through a web interface. Its startup script automatically prepares the environment by creating a sandbox directory, installing key ML libraries, and launching the agent interface. The system is tightly integrated with... -
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OpenMLSys-ZH
Machine Learning Systems: Design and Implementation
This repository is the Chinese translation (or localization) of the OpenMLSys project documentation. Its aim is to make the technical content, tutorials, architecture descriptions, and user guides of the OpenMLSys system more accessible to Chinese-speaking users. The repo mirrors the structure of the original OpenMLSys docs: sections on system design, API references, deployment instructions, module overviews, and example workflows. It helps bridge language barriers in open machine learning... -
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Optax
Optax is a gradient processing and optimization library for JAX
Optax is a gradient processing and optimization library for JAX. It is designed to facilitate research by providing building blocks that can be recombined in custom ways in order to optimize parametric models such as, but not limited to, deep neural networks. We favor focusing on small composable building blocks that can be effectively combined into custom solutions. Others may build upon these basic components in more complicated abstractions. Whenever reasonable, implementations prioritize... -
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Sacred
Sacred is a tool to help you configure, andorganize IDSIA experiments
Sacred is a tool to help you configure, organize, log and reproduce experiments. It is designed to do all the tedious overhead work that you need to do around your actual experiment. A very convenient way of the local variables in a function to define the parameters your experiment uses. You can access all parameters of your configuration from every function. They are automatically injected by name. You get a powerful command-line interface for each experiment that you can use to change... -
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flair
A very simple framework for state-of-the-art NLP
A very simple framework for state-of-the-art NLP. Developed by Humboldt University of Berlin and friends. A powerful NLP library. Flair allows you to apply our state-of-the-art natural language processing (NLP) models to your text, such as named entity recognition (NER), sentiment analysis, part-of-speech tagging (PoS), special support for biomedical texts, sense disambiguation and classification, with support for a rapidly growing number of languages. A text embedding library. Flair has... -
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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... -
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torchtext
Data loaders and abstractions for text and NLP
We recommend Anaconda as a Python package management system. Please refer to pytorch.org for the details of PyTorch installation. LTS versions are distributed through a different channel than the other versioned releases. Alternatively, you might want to use the Moses tokenizer port in SacreMoses (split from NLTK). You have to install SacreMoses. To build torchtext from source, you need git, CMake and C++11 compiler such as g++. -
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SpikingJelly
SpikingJelly is an open-source deep learning framework
SpikingJelly is an open-source deep learning framework for spiking neural networks that is primarily built on top of PyTorch and aimed at neuromorphic computing research. The project provides the components needed to build, train, and evaluate neural models that communicate through discrete spikes rather than the continuous activations used in conventional artificial neural networks. This makes it especially relevant for researchers interested in biologically inspired computing, event-driven... -
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Diffrax
Numerical differential equation solvers in JAX
Diffrax is a numerical differential equation solving library built for the JAX ecosystem, with a strong focus on composability, differentiability, and high-performance scientific computing. The project provides tools for solving ordinary differential equations, stochastic differential equations, controlled differential equations, and related systems in a way that fits naturally into modern machine learning and differentiable programming workflows. Because it is written to work closely with... -
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AI-Tutorials/Implementations Notebooks
Codes/Notebooks for AI Projects
AI-Tutorials/Implementations Notebooks repository is a comprehensive collection of artificial intelligence tutorials and implementation examples intended for developers, students, and researchers who want to learn by building practical AI projects. The repository contains numerous Jupyter notebooks and code samples that demonstrate modern techniques in machine learning, deep learning, data science, and large language model workflows. It includes implementations for a wide range of AI topics...
