Search Results for "python-ldap" - Page 91
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Showing 2756 open source projects for "python-ldap"
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FARM
Fast & easy transfer learning for NLP
FARM makes Transfer Learning with BERT & Co simple, fast and enterprise-ready. It's built upon transformers and provides additional features to simplify the life of developers: Parallelized preprocessing, highly modular design, multi-task learning, experiment tracking, easy debugging and close integration with AWS SageMaker. With FARM you can build fast proofs-of-concept for tasks like text classification, NER or question answering and transfer them easily into production. Easy fine-tuning... -
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Transformer TTS
Implementation of a Transformer based neural network
TransformerTTS is an implementation of a non-autoregressive Transformer-based neural network for text-to-speech, built with TensorFlow 2. It takes inspiration from architectures like FastSpeech, FastSpeech 2, FastPitch, and Transformer TTS, and extends them with its own aligner and forward models. The system separates alignment learning and acoustic modeling: an autoregressive Transformer is used as an aligner to extract phoneme-to-frame durations, while a non-autoregressive... -
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Minkowski Engine
Auto-diff neural network library for high-dimensional sparse tensors
The Minkowski Engine is an auto-differentiation library for sparse tensors. It supports all standard neural network layers such as convolution, pooling, unspooling, and broadcasting operations for sparse tensors. The Minkowski Engine supports various functions that can be built on a sparse tensor. We list a few popular network architectures and applications here. To run the examples, please install the package and run the command in the package root directory. Compressing a neural network to... -
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AliceMind
ALIbaba's Collection of Encoder-decoders from MinD
This repository provides pre-trained encoder-decoder models and its related optimization techniques developed by Alibaba's MinD (Machine IntelligeNce of Damo) Lab. Pre-trained models for natural language understanding (NLU). We extend BERT to a new model, StructBERT, by incorporating language structures into pre-training. Specifically, we pre-train StructBERT with two auxiliary tasks to make the most of the sequential order of words and sentences, which leverage language structures at the... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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TimeSformer
The official pytorch implementation of our paper
TimeSformer is a vision transformer architecture for video that extends the standard attention mechanism into spatiotemporal attention. The model alternates attention along spatial and temporal dimensions (or designs variants like divided attention) so that it can capture both appearance and motion cues in video. Because the attention is global across frames, TimeSformer can reason about dependencies across long time spans, not just local neighborhoods. The official implementation in PyTorch... -
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hebrew-gpt_neo
Hebrew text generation models based on EleutherAI's gpt-neo
Hebrew text generation models based on EleutherAI's gpt-neo. Each was trained on a TPUv3-8 which was made available to me via the TPU Research Cloud Program. The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture. -
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Hands-on Unsupervised Learning
Code for Hands-on Unsupervised Learning Using Python (O'Reilly Media)
...Unsupervised learning can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel provides practical knowledge on how to apply unsupervised learning using two simple, production-ready Python frameworks - scikit-learn and TensorFlow. With the hands-on examples and code provided, you will identify difficult-to-find patterns in data. -
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SRU
Training RNNs as Fast as CNNs
Common recurrent neural architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations. In this work, we propose the Simple Recurrent Unit (SRU), a light recurrent unit that balances model capacity and scalability. SRU is designed to provide expressive recurrence, enable highly parallelized implementation, and comes with careful initialization to facilitate the training of deep models. We demonstrate the effectiveness of SRU on multiple NLP tasks. SRU... -
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PyCls
Codebase for Image Classification Research, written in PyTorch
pycls is a focused PyTorch codebase for image classification research that emphasizes reproducibility and strong, transparent baselines. It popularized families like RegNet and supports classic architectures (ResNet, ResNeXt) with clean implementations and consistent training recipes. The repository includes highly tuned schedules, augmentations, and regularization settings that make it straightforward to match reported accuracy without guesswork. Distributed training and mixed precision are... -
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EH Forwarder Bot
An extensible message tunneling chat bot framework
Codename EH Forwarder Bot (EFB) is an extensible message tunneling chat bot framework that delivers messages to and from multiple platforms and remotely controls your accounts. Since the majority of our channels are using polling for message retrieval, a stable internet connection is necessary for channels to run smoothly. An unstable connection may lead to slow response, or loss of messages. EFB uses a *nix user configuration style, which is described in details in Directories. In short, if... -
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Machine Learning Collection
A resource for learning about Machine learning & Deep Learning
A resource for learning about Machine learning & Deep Learning. In this repository, you will find tutorials and projects related to Machine Learning. I try to make the code as clear as possible, and the goal is be to used as a learning resource and a way to look up problems to solve specific problems. For most, I have also done video explanations on YouTube if you want a walkthrough for the code. -
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tensorflow_template_application
TensorFlow template application for deep learning
tensorflow_template_application is a template project that demonstrates how to structure scalable applications built with TensorFlow. The repository provides a standardized architecture that helps developers organize machine learning code into clear components such as data processing, model training, evaluation, and deployment. Instead of focusing on a specific algorithm, the project emphasizes software engineering practices that make machine learning systems easier to maintain and extend.... -
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SimCSE
SimCSE: Simple Contrastive Learning of Sentence Embeddings
SimCSE (Simple Contrastive Learning of Sentence Embeddings) is a machine learning framework for training sentence embeddings using contrastive learning. It improves representation learning for NLP tasks. -
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jiant
jiant is an nlp toolkit
Jiant is a multitask NLP framework for fine-tuning transformer-based models on multiple natural language understanding (NLU) tasks. -
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DrQA
Reading Wikipedia to Answer Open-Domain Questions
DrQA is an open-domain question answering system that reads large text corpora—famously Wikipedia—to answer natural language questions with extractive spans. It follows a two-stage pipeline: a fast document retriever first narrows down candidate articles, and a neural machine reader then predicts the exact answer span from those passages. The retriever relies on classic IR features (like TF-IDF and n-gram statistics) to remain lightweight and scalable to millions of documents. The reader is... -
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Opyrator
Turns your machine learning code into microservices with web API
Instantly turn your Python functions into production-ready microservices. Deploy and access your services via HTTP API or interactive UI. Seamlessly export your services into portable, shareable, and executable files or Docker images. Opyrator builds on open standards - OpenAPI, JSON Schema, and Python type hints - and is powered by FastAPI, Streamlit, and Pydantic. -
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TensorFlow.js models
Pretrained models for TensorFlow.js
...New models should have a test NPM script. You can run the unit tests for any of the models by running "yarn test" inside a directory. Use off-the-shelf JavaScript models or convert Python TensorFlow models to run in the browser or under Node.js. Build and train models directly in JavaScript using flexible and intuitive APIs. Develop ML models in JavaScript, and use ML directly in the browser or in Node.js. -
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UniVL
Official implementation for UniVL video and language training models
UniVL is a video-language pretrain model. It is designed with four modules and five objectives for both video language understanding and generation tasks. It is also a flexible model for most of the multimodal downstream tasks considering both efficiency and effectiveness. -
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Multi-Agent path planning in Python
Python implementation of a bunch of multi-robot path-planning
multi_agent_path_planning is a Python-based implementation of multi-agent pathfinding algorithms for coordinating multiple agents in shared environments without collisions. It is useful in robotics, warehouse automation, and gaming AI. -
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deep-learning-for-image-processing
deep learning for image processing including classification
deep-learning-for-image-processing is an extensive educational repository covering practical deep learning methods for computer vision. It organizes implementations, explanations, presentation files, and video lessons around major neural network architectures. The material teaches both model structure and training workflows, with examples built in PyTorch and TensorFlow through Keras. Classification topics range from LeNet and AlexNet to ResNet, EfficientNet, Vision Transformer, Swin... -
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DeepSpeech
Open source embedded speech-to-text engine
DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers. DeepSpeech is an open-source Speech-To-Text engine, using a model trained by machine learning techniques based on Baidu's Deep Speech research paper. Project DeepSpeech uses Google's TensorFlow to make the implementation easier. A pre-trained English model is available for use and can be downloaded following the... -
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DensePose
A real-time approach for mapping all human pixels of 2D RGB images
DensePose is a computer vision system that maps all human pixels in an RGB image to the 3D surface of a human body model. It extends human pose estimation from predicting joint keypoints to providing dense correspondences between 2D images and a canonical 3D mesh (such as the SMPL model). This enables detailed understanding of human shape, motion, and surface appearance directly from images or videos. The repository includes the DensePose network architecture, training code, pretrained... -
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CapsGNN
A PyTorch implementation of "Capsule Graph Neural Network"
A PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019). The high-quality node embeddings learned from the Graph Neural Networks (GNNs) have been applied to a wide range of node-based applications and some of them have achieved state-of-the-art (SOTA) performance. However, when applying node embeddings learned from GNNs to generate graph embeddings, the scalar node representation may not suffice to preserve the node/graph properties efficiently, resulting in sub-optimal graph... -
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Denoiser
Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)
Denoiser is a real-time speech enhancement model operating directly on raw waveforms, designed to clean noisy audio while running efficiently on CPU. It uses a causal encoder-decoder architecture with skip connections, optimized with losses defined both in the time domain and frequency domain to better suppress noise while preserving speech. Unlike models that operate on spectrograms alone, this design enables lower latency and coherent waveform output. The implementation includes data... -
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Couler
Unified Interface for Constructing and Managing Workflows
Couler is a system designed for unified machine learning workflow optimization in the cloud. Couler endeavors to provide a unified interface for constructing and optimizing workflows across various workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow. Couler enhances workflow efficiency through features like Autonomous Workflow Construction, Automatic Artifact Caching Mechanisms, Big Workflow Auto Parallelism Optimization, and Automatic Hyperparameters Tuning.
