Search Results for "git:/git.code.sf.net/p/docfetcher/code" - Page 45
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Showing 1618 open source projects for "git:/git.code.sf.net/p/docfetcher/code"
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Get full visibility and control over your tasks and projects with Wrike.Wrike offers world-class features that empower cross-functional, distributed, or growing teams take their projects from the initial request stage all the way to tracking work progress and reporting results.
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Transforming NetOps Through No-Code Network Automation - NetBrainNetBrain, founded in 2004, provides a powerful no-code automation platform for hybrid network observability, allowing organizations to enhance their operational efficiency through automated workflows. The platform applies automation across three key workflows: troubleshooting, change management, and assessment.
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1
Guided Diffusion
Codebase for Diffusion Models Beat GANS on Image Synthesis
...It is derived from OpenAI’s improved-diffusion work, enhanced to include guided generation where a classifier (or other guidance mechanism) can steer sampling toward desired classes or attributes. The code provides model definitions (UNet, diffusion schedules), sampling and training scripts, and utilities for guidance and evaluation. A key insight is that combining diffusion sampling with classifier gradients allows fine control over the generated images, trading off diversity vs fidelity. The repository includes scripts such as image_train.py, image_sample.py, and classifier_train.py to train diffusion models, generate samples, and train guiding classifiers. ... -
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ConvNeXt
Code release for ConvNeXt model
ConvNeXt is a modernized convolutional neural network (CNN) architecture designed to rival Vision Transformers (ViTs) in accuracy and scalability while retaining the simplicity and efficiency of CNNs. It revisits classic ResNet-style backbones through the lens of transformer design trends—large kernel sizes, inverted bottlenecks, layer normalization, and GELU activations—to bridge the performance gap between convolutions and attention-based models. ConvNeXt’s clean, hierarchical structure... -
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ISLR-python
An Introduction to Statistical Learning
...The datasets used in the book are also included so that users can run experiments directly within the provided notebooks. By translating the statistical learning material into Python code, the repository makes the book’s concepts accessible to a wider community of Python users. -
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nlpaug
Data augmentation for NLP
This Python library helps you with augmenting nlp for your machine learning projects. Visit this introduction to understand Data Augmentation in NLP. Augmenter is the basic element of augmentation while Flow is a pipeline to orchestra multi augmenters together. -
Strengthen your current Business intelligence infrastructure by automating reports and manual tasksPBRS™ and CRD®, our standalone report scheduling solutions for Power BI, Microsoft SQL Server Reporting Services® (SSRS) and SAP Crystal Reports®, are designed to supplement and strengthen your current Business intelligence infrastructure by automating reports & manual tasks, layering useful incremental capabilities, and scaling capacity by orders of magnitude. They supercharge operational productivity while lowering administrative costs.
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Alphafold2
Unofficial Pytorch implementation / replication of Alphafold2
...Once this is replicated, I intend to fold all available amino acid sequences out there in-silico and release it as an academic torrent, to further science. Deepmind has open sourced the official code in Jax, along with the weights! This repository will now be geared towards a straight pytorch translation with some improvements on positional encoding. lhatsk has reported training a modified trunk of this repository, using the same setup as trRosetta, with competitive results. The underlying assumption is that the trunk works on the residue level, and then constitutes to atomic level for the structure module, whether it be SE3 Transformers, E(n)-Transformer, or EGNN doing the refinement. -
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flutter_ume
UME is an in-app debug kits platform for Flutter
...It lets developers embed a suite of debugging tools directly into a Flutter app (during development or debug builds), enabling inspection, performance monitoring, UI debugging, network request inspection, widget hierarchy introspection, and more — all from within the running app. UME bundles multiple “plugin kits” (e.g., UI inspector, performance monitor, device info panel, network inspector via Dio, console log viewer, code viewer, touch indicator) so developers can enable only the tools they need. It supports Flutter 3 and Dart with null-safety, integrates as a dev dependency (so it doesn’t ship by default with production builds), and offers a simple API to activate the debug overlay. Because it's open source under MIT license, developers can also create custom plugin kits and extend UME’s capabilities to suit their debugging or testing workflow. -
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Machine Learning Glossary
Machine learning glossary
...The content is organized into sections that progressively introduce key ideas from basic machine learning concepts to more advanced mathematical topics. Many pages include diagrams or code examples to illustrate how algorithms work in practice. Because the project emphasizes accessibility, it is particularly useful for beginners who want a conceptual overview of machine learning terminology before diving into more technical research papers. -
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WaveRNN
WaveRNN Vocoder + TTS
...As a vocoder, WaveRNN models raw audio with a compact recurrent neural network that can generate high-quality waveforms more efficiently than many traditional autoregressive models. The repository includes scripts and code for preprocessing datasets such as LJSpeech, training Tacotron to produce mel spectrograms, training WaveRNN on those spectrograms (with optional GTA data), and finally generating audio. A quick_start.py script allows users to immediately synthesize example sentences from a pretrained model and inspect both generated audio and attention plots. ... -
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Fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python
Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks. We provide reference implementations of various sequence modeling papers. Recent work by Microsoft and Google has shown that data parallel training can be made significantly more efficient by sharding the model parameters and optimizer state across data parallel workers. These ideas are encapsulated in the... -
Eurekos LMS - Build a Smarter CustomerEurekos is a purpose-built LMS that engages customers throughout the entire learning journey from pre-sales, to onboarding, and everything after.
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tf2_course
Notebooks for my "Deep Learning with TensorFlow 2 and Keras" course
...The material is intended for learners who already have foundational knowledge of ML and wish to deepen their understanding of deep learning frameworks and practices. The repo supports experimentation: you can run code, tweak hyperparameters, and follow guided exercises that strengthen practical mastery. Rather than being book-based, it is course-based, meaning the flow, examples and structure lean toward interactive teaching and incremental builds. It’s well-suited for those who want a focused, deep-learning path rather than a broad ML textbook. -
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AI Atelier
Based on the Disco Diffusion, version of the AI art creation software
Based on the Disco Diffusion, we have developed a Chinese & English version of the AI art creation software "AI Atelier". We offer both Text-To-Image models (Disco Diffusion and VQGAN+CLIP) and Text-To-Text (GPT-J-6B and GPT-NEOX-20B) as options. Making available complete source code of licensed works and modifications, which include larger works using a licensed work, under the same license. Copyright and license notices must be preserved. When a modified version is used to provide a service over a network, the complete source code of the modified version must be made available. Create 2D and 3D animations and not only still frames (from Disco Diffusion v5 and VQGAN Animations). ... -
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pyprobml
Python code for "Probabilistic Machine learning" book by Kevin Murphy
...See also probml-utils for some utility code that is shared across multiple notebooks. -
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Mask2Former
Code release for "Masked-attention Mask Transformer
Mask2Former is a unified segmentation architecture that handles semantic, instance, and panoptic segmentation with one model and one training recipe. Its core idea is to cast segmentation as mask classification: a transformer decoder predicts a set of mask queries, each with an associated class score, eliminating the need for task-specific heads. A pixel decoder fuses multi-scale features and feeds masked attention in the transformer so each query focuses computation on its current spatial... -
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Catalyst
Accelerated deep learning R&D
Catalyst is a PyTorch framework for accelerated Deep Learning research and development. It allows you to write compact but full-featured Deep Learning pipelines with just a few lines of code. With Catalyst you get a full set of features including a training loop with metrics, model checkpointing and more, all without the boilerplate. Catalyst is focused on reproducibility, rapid experimentation, and codebase reuse so you can break the cycle of writing another regular train loop and make something totally new. Catalyst is compatible with Python 3.6+. ... -
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TensorFlowOnSpark
TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters
By combining salient features from the TensorFlow deep learning framework with Apache Spark and Apache Hadoop, TensorFlowOnSpark enables distributed deep learning on a cluster of GPU and CPU servers. It enables both distributed TensorFlow training and inferencing on Spark clusters, with a goal to minimize the amount of code changes required to run existing TensorFlow programs on a shared grid. -
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DeeProtGO
DeeProtGO is a deep learning model for predicting GO terms of proteins
This project contains the source code of DeeProtGO as well as an example of its use when predicting GO terms of the biological process sub-ontology for eukaryotic proteins. -
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The fastai book
The fastai book, published as Jupyter Notebooks
...These notebooks are used for a MOOC and form the basis of this book, which is currently available for purchase. It does not have the same GPL restrictions that are on this repository. The code in the notebooks and python .py files is covered by the GPL v3 license; see the LICENSE file for details. The remainder (including all markdown cells in the notebooks and other prose) is not licensed for any redistribution or change of format or medium, other than making copies of the notebooks or forking this repo for your own private use. ... -
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RQ-Transformer
Implementation of RQ Transformer, autoregressive image generation
...A short sequence length is important for an AR model to reduce its computational costs to consider long-range interactions of codes. However, we postulate that previous VQ cannot shorten the code sequence and generate high-fidelity images together in terms of the rate-distortion trade-off. -
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LayoutParser
A Unified Toolkit for Deep Learning Based Document Image Analysis
With the help of state-of-the-art deep learning models, Layout Parser enables extracting complicated document structures using only several lines of code. This method is also more robust and generalizable as no sophisticated rules are involved in this process. A complete instruction for installing the main Layout Parser library and auxiliary components. Learn how to load DL Layout models and use them for layout detection. The full list of layout models currently available in Layout Parser. ... -
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PyTorch Handbook
The pytorch handbook is an open source book
...The material is written with a practical focus so that readers can follow along and run the provided examples successfully. Each tutorial is tested to ensure that the code runs correctly, making the repository particularly useful for beginners who want reliable learning materials. The handbook emphasizes hands-on learning through real code examples rather than purely theoretical explanations. -
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EasyNLP
EasyNLP: A Comprehensive and Easy-to-use NLP Toolkit
...It is seamlessly integrated to Platform of AI (PAI) products, including PAI-DSW for development, PAI-DLC for cloud-native training, PAI-EAS for serving, and PAI-Designer for zero-code model training. -
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bert4keras
Keras implement of transformers for humans
...A cleaner, lighter version of bert for keras. This is the keras version of the transformer model library re-implemented by the author and is committed to combining transformer and keras with as clean code as possible. The original intention of this project is for the convenience of modification and customization, so it may be updated frequently. Load the pre-trained weights of bert/roberta/albert for fine-tune. Implement the attention mask required by the language model and seq2seq. Pre-training code from zero (supports TPU, multi-GPU, please see pertaining). ... -
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TensorFlow Backend for ONNX
Tensorflow Backend for ONNX
Open Neural Network Exchange (ONNX) is an open standard format for representing machine learning models. ONNX is supported by a community of partners who have implemented it in many frameworks and tools. TensorFlow Backend for ONNX makes it possible to use ONNX models as input for TensorFlow. The ONNX model is first converted to a TensorFlow model and then delegated for execution on TensorFlow to produce the output. This is one of the two TensorFlow converter projects which serve different... -
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Deep Learning course
Slides and Jupyter notebooks for the Deep Learning lectures
Slides and Jupyter notebooks for the Deep Learning lectures at Master Year 2 Data Science from Institut Polytechnique de Paris. This course is being taught at as part of Master Year 2 Data Science IP-Paris. Note: press "P" to display the presenter's notes that include some comments and additional references. This lecture is built and maintained by Olivier Grisel and Charles Ollion. -
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YOLOv3
Object detection architectures and models pretrained on the COCO data
...Export and deploy your YOLOv5 model with just 1 line of code. There are also loads of quickstart guides and tutorials available to get your model where it needs to be. Create state of the art deep learning models with YOLOv5
