Search Results for "python-4suite-xml" - Page 20
Sort By:
Showing 987 open source projects for "python-4suite-xml"
View related business solutions-
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
-
1
BMC
Notes on Scientific Computing for Biomechanics
This repository is a collection of lecture notes and code on scientific computing and data analysis for Biomechanics and Motor Control. -
2
Brain AutoML
Google Brain AutoML
...Google Brain researchers have introduced a new way of programming automated machine learning (AutoML) based on symbolic programming. The researchers also proposed PyGlove, a general symbolic programming library for Python, to implement the symbolic formulation of AutoML. AutoML, designed to fill the machine learning industry’s talent gap, is gaining traction among various organizations. -
3
.Net Core for Delphi/Lazarus
The best library to work with the .Net Core from Delphi and Lazarus.
The .Net Core for Delphi/Lazarus (dotNetCore4Delphi) is a .Net Core Runtime Library for Delphi which helps Delphi/Pascal developers work with .Net Core. It is designed to provide a way to interact with .Net Core applications from Delphi/Pascal. What dotNetCore4Delphi can do: - Access .Net Core Framework Class Library. - Access Third Party .Net Core Libraries. - Access Your .Net Core Libraries. - etc Competitive Advantages: - Cross-platform framework that runs on Windows, Linux and... -
4
Awesome Graph Classification
Graph embedding, classification and representation learning papers
A collection of graph classification methods, covering embedding, deep learning, graph kernel and factorization papers with reference implementations. Relevant graph classification benchmark datasets are available. Similar collections about community detection, classification/regression tree, fraud detection, Monte Carlo tree search, and gradient boosting papers with implementations. -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
-
5
cnn-for-image-retrieval is a research-oriented project that demonstrates the use of convolutional neural networks (CNNs) for image retrieval tasks. The repository provides implementations of CNN-based methods to extract feature representations from images and use them for similarity-based retrieval. It focuses on applying deep learning techniques to improve upon traditional handcrafted descriptors by learning features directly from data. The code includes training and evaluation scripts that...
-
6
BeaEngine 5
BeaEngine disasm project
BeaEngine is a C library designed to decode instructions from 16-bit, 32-bit and 64-bit intel architectures. It includes standard instructions set and instructions set from FPU, MMX, SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2, VMX, CLMUL, AES, MPX, AVX, AVX2, AVX512 (VEX & EVEX prefixes), CET, BMI1, BMI2, SGX, UINTR, KL, TDX and AMX extensions. If you want to analyze malicious codes and more generally obfuscated codes, BeaEngine sends back a complex structure that describes precisely the... -
7
NLP Architect
A model library for exploring state-of-the-art deep learning
NLP Architect is an open-source Python library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing and Natural Language Understanding neural networks. The library includes our past and ongoing NLP research and development efforts as part of Intel AI Lab. NLP Architect is designed to be flexible for adding new models, neural network components, data handling methods, and for easy training and running models. -
8
gradslam
gradslam is an open source differentiable dense SLAM library
gradslam is an open-source framework providing differentiable building blocks for simultaneous localization and mapping (SLAM) systems. We enable the usage of dense SLAM subsystems from the comfort of PyTorch. The question of “representation” is central in the context of dense simultaneous localization and mapping (SLAM). Newer learning-based approaches have the potential to leverage data or task performance to directly inform the choice of representation. However, learning representations... -
9
Hack-Tools
Hack tools
hack-tools is a collection of various hacking tools and utilities. It serves as a comprehensive toolkit for penetration testers and cybersecurity enthusiasts, encompassing a wide range of functionalities. -
$300 Free Credits to Build on Google CloudStart your next project with $300 in free Google Cloud credit. Spin up VMs, run containers, query petabytes in BigQuery, or build agents with Gemini Enterprise Agent Platform. Once your credits are used, keep building with 20+ always-free tier products including Compute Engine, Cloud Storage, GKE, and Cloud Run functions. No commitment required—just sign up and start building.
-
10
TFLearn
Deep learning library featuring a higher-level API for TensorFlow
TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed up experimentations while remaining fully transparent and compatible with it. Easy-to-use and understand high-level API for implementing deep neural networks, with tutorials and examples. Fast prototyping through highly modular built-in neural network layers, regularizers, optimizers, and metrics. Full transparency... -
11
fastNLP
fastNLP: A Modularized and Extensible NLP Framework
fastNLP is a lightweight framework for natural language processing (NLP), the goal is to quickly implement NLP tasks and build complex models. A unified Tabular data container simplifies the data preprocessing process. Built-in Loader and Pipe for multiple datasets, eliminating the need for preprocessing code. Various convenient NLP tools, such as Embedding loading (including ELMo and BERT), intermediate data cache, etc.. Provide a variety of neural network components and recurrence models... -
12
Zipline
Zipline, a Pythonic algorithmic trading library
...Quantopian also offers a fully managed service for professionals that includes Zipline, Alphalens, Pyfolio, FactSet data, and more. Installing Zipline is slightly more involved than the average Python package. For a development installation (used to develop Zipline itself), create and activate a virtualenv, then run the etc/dev-install script. Please note that Zipline is not a community-led project. Zipline is maintained by the Quantopian engineering team, and we are quite small and often busy. -
13
StructPie
A set of C libraries to implement data structures and algorithms
...While in "HashBSTree" directory, a hash table with a binary tree in each index is implemented for faster lookup in large data. The stack, the tree and the hash table accept int, float and char* data type To look at the python library : https://github.com/mnoorfawi/struct-pie -
14
SentEval
A python tool for evaluating the quality of sentence embeddings
SentEval is a standardized toolkit for evaluating sentence embeddings across a wide spectrum of downstream tasks and probing tests. It defines a simple interface—provide an encoder function from sentences to vectors—and then runs consistent training/evaluation loops for tasks like sentiment, entailment, paraphrase, and semantic textual similarity. The suite also contains linguistic probing tasks that illuminate what properties embeddings capture, such as tense, word order, or syntactic... -
15
SageMaker MXNet Training Toolkit
Toolkit for running MXNet training scripts on SageMaker
...For the Dockerfiles used for building SageMaker MXNet Containers, see AWS Deep Learning Containers. For information on running MXNet jobs on Amazon SageMaker, please refer to the SageMaker Python SDK documentation. With the SDK, you can train and deploy models using popular deep learning frameworks Apache MXNet and TensorFlow. You can also train and deploy models with Amazon algorithms, which are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training. If you have your own algorithms built into SageMaker compatible Docker containers, you can train and host models using these as well. -
16
Json Node Tree Library
Represents JSON doc as a nodes tree for transforms like XPATH & XSLT.
Json Node Tree framework is provisioning of transformation operations for JSON documents similar XML XPATH and XSLT. The framework parses textual JSON document to nodes tree that represents JSON entity: object, array or value of primitive type. Generated tree allows to modify values of nodes and/or structure of the given JSON document. Representation of JSON object as a nodes tree allows implement an operations such as path query and transformations (like XPATH and XSLT for XML). -
17
CSharpWriter
A C# Control use to edit RTF can replace RichTextBox.
CSharpWriter(short name CSWriter) is a new RTF style document process engine without RichTextBox control. it is more powerfull than RichTextBox.We holpe it can replace OpenOffice in .NET World. -
18
PyQDbf
PySide QDbf
PyQDbf PySide - QDbf Binding LGPL3 QDbf is Qt - DBF files https://github.com/IvanPinezhaninov/qdbf QDbf: Read or Write Dbf files, but not create new table PySide is Python Binding for Qt Libraries -
19
Sparse Attention
"Generating Long Sequences with Sparse Transformers" examples
Sparse Attention is OpenAI’s code release for the Sparse Transformer model, introduced in the paper Generating Long Sequences with Sparse Transformers. It explores how modifying the self-attention mechanism with sparse patterns can reduce the quadratic scaling of standard transformers, making it possible to model much longer sequences efficiently. The repository provides implementations of sparse attention layers, training code, and evaluation scripts for benchmark datasets. It highlights... -
20
bluetroller
A library and interface for controlling bluetooth LE devices
bluetroller is a library and interface for controlling all kinds of bluetooth LE devices. A vast number of devices can be controlled via Bluetooth LE, including fitness trackers, lighting, camera sliders, gimbals and many more. Right now these devices can only be controlled via phone apps which are frequently buggy, unmaintained and will stop working after some future phone update. This project aims to grow to become an exhaustive library of these devices. -
21
Several language bindings for the FTDI D2XX driver used in FTDI's USB products. Currently supported languages are Python (pyd2xx), Java (jd2xx), CSharp (csd2xx) and LabVIEW (lvd2xx).
-
22
Ansible Examples
A few starter examples of ansible playbooks, to show features
This repository collects practical, real-world examples of using Ansible to automate infrastructure, deployments, and configurations. Each directory demonstrates a specific use case—ranging from setting up web servers, load balancers, and databases to orchestrating multi-tier applications in cloud environments. The examples highlight common Ansible practices such as organizing inventories, writing reusable playbooks, using roles, and handling variables and templates. They’re designed to be... -
23
MMdnn
Tools to help users inter-operate among deep learning frameworks
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML. MMdnn is a comprehensive and cross-framework tool to convert, visualize and diagnose deep learning (DL) models. The "MM" stands for model management, and "dnn" is the acronym of deep neural network. We implement a universal converter to convert DL models between frameworks,... -
24
Forecasting Best Practices
Time Series Forecasting Best Practices & Examples
...Rather than creating implementations from scratch, we draw from existing state-of-the-art libraries and build additional utilities around processing and featuring the data, optimizing and evaluating models, and scaling up to the cloud. The examples and best practices are provided as Python Jupyter notebooks and R markdown files and a library of utility functions. -
25
Higher
higher is a pytorch library
higher is a specialized library designed to extend PyTorch’s capabilities by enabling higher-order differentiation and meta-learning through differentiable optimization loops. It allows developers and researchers to compute gradients through entire optimization processes, which is essential for tasks like meta-learning, hyperparameter optimization, and model adaptation. The library introduces utilities that convert standard torch.nn.Module instances into “stateless” functional forms, so...
