Search Results for "g-code" - Page 7
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Showing 346 open source projects for "g-code"
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Adaptive Intelligence
Adaptive Intelligence also known as "Artificial General Intelligence"
Adaptive Intelligence is the implementation of neural science, forensic psychology , behavioral science with machine-learning and artificial intelligence to provide advanced automated software platforms with the ability to adjust and thrive in dynamic environments by combining cognitive flexibility, emotional regulation, resilience, and practical problem-solving skills. -
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TensorFlow Privacy
Library for training machine learning models with privacy for data
Library for training machine learning models with privacy for training data. This repository contains the source code for TensorFlow Privacy, a Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy. The library comes with tutorials and analysis tools for computing the privacy guarantees provided. -
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SSD in PyTorch 1.0
High quality, fast, modular reference implementation of SSD in PyTorch
This repository implements SSD (Single Shot MultiBox Detector). The implementation is heavily influenced by the projects ssd.pytorch, pytorch-ssd and maskrcnn-benchmark. This repository aims to be the code base for research based on SSD. Multi-GPU training and inference: We use DistributedDataParallel, you can train or test with arbitrary GPU(s), the training schema will change accordingly. Add your own modules without pain. We abstract backbone, Detector, BoxHead, BoxPredictor, etc. You can replace every component with your own code without changing the code base. ... -
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PromptTools
Open-source tools for prompt testing and experimentation
...This repo offers a set of open-source, self-hostable tools for experimenting with, testing, and evaluating LLMs, vector databases, and prompts. The core idea is to enable developers to evaluate using familiar interfaces like code, notebooks, and a local playground. -
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Sonnet
TensorFlow-based neural network library
...Users are also encouraged to build their own modules. Sonnet is designed to be extremely unopinionated about your use of modules. It is simple to understand, and offers clear and focused code. -
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ICCV2023-Paper-Code-Interpretation
ICCV2021/2019/2017 Paper/Code/Interpretation/Live Broadcast Collection
ICCV2023-Paper-Code-Interpretation is a curated repository that provides explanations and interpretations of code associated with research papers presented at the International Conference on Computer Vision (ICCV) 2023. The project focuses on helping researchers and students better understand how complex computer vision algorithms described in academic papers are implemented in practice. -
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TensorFlow.NET
.NET Standard bindings for Google's TensorFlow for developing models
...SciSharp STACK's mission is to bring popular data science technology into the .NET world and to provide .NET developers with a powerful Machine Learning tool set without reinventing the wheel. Since the APIs are kept as similar as possible you can immediately adapt any existing TensorFlow code in C# or F# with a zero learning curve. Take a look at a comparison picture and see how comfortably a TensorFlow/Python script translates into a C# program with TensorFlow.NET. -
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Taylorplot_Neptune
Creation of a Taylorplot for several machine learning models
Here we present the lines of code for creating a taylor plot with python to display several machine learning models. We show the solution for displaying 10 models, but the list and number can be changed simply by modifying the sample list. -
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SageMaker Inference Toolkit
Serve machine learning models within a Docker container
...You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. Once you have a trained model, you can include it in a Docker container that runs your inference code. A container provides an effectively isolated environment, ensuring a consistent runtime regardless of where the container is deployed. Containerizing your model and code enables fast and reliable deployment of your model. The SageMaker Inference Toolkit implements a model serving stack and can be easily added to any Docker container, making it deployable to SageMaker. ... -
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spaGO
Self-contained Machine Learning and Natural Language Processing lib
...A good place to start is by looking at the implementation of built-in neural models, such as the LSTM. Except for a few linear algebra operations written in assembly for optimal performance (a bit of copying from Gonum), it's straightforward Go code, so you don't have to worry. -
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Asteroid
The PyTorch-based audio source separation toolkit for researchers
The PyTorch-based audio source separation toolkit for researchers. Pytorch-based audio source separation toolkit that enables fast experimentation on common datasets. It comes with a source code thats supports a large range of datasets and architectures, and a set of recipes to reproduce some important papers. Building blocks are thought and designed to be seamlessly plugged together. Filterbanks, encoders, maskers, decoders and losses are all common building blocks that can be combined in a flexible way to create new systems. ... -
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Paper-with-Code-of-Wireless-comm
Paper-with-Code-of-Wireless-communication-Based-on-DL
Paper-with-Code-of-Wireless-communication-Based-on-DL is a curated repository that collects research papers and corresponding code implementations related to the application of deep learning in wireless communication systems. The project aims to help researchers and graduate students quickly find reproducible implementations of algorithms used in modern communication research. -
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The Algorithms - C #
Collection of various algorithms in mathematics, machine learning
TheAlgorithms/C is an open-source repository that provides implementations of classic algorithms and data structures written in the C programming language. The project is part of the larger “The Algorithms” initiative, which aims to create educational resources by implementing algorithms in multiple programming languages. Within the C repository, contributors implement algorithms from many areas of computer science including sorting, searching, graph processing, mathematics, machine... -
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fastquant
Backtest and optimize your ML trading strategies with only 3 lines
fastquant is a Python library designed to simplify quantitative financial analysis and algorithmic trading strategy development. The project focuses on making backtesting accessible by providing a high-level interface that allows users to test investment strategies with only a few lines of code. It integrates historical market data sources and trading frameworks so that users can quickly build experiments without constructing complex data pipelines. The framework enables users to test common strategies such as moving average crossovers, momentum trading, and custom indicators on historical stock data. By automating data retrieval, strategy evaluation, and result visualization, the library reduces the barrier to entry for individuals interested in quantitative finance. ... -
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D2L.ai
Interactive deep learning book with multi-framework code
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 300 universities from 55 countries including Stanford, MIT, Harvard, and Cambridge. This open-source book represents our attempt to make deep learning approachable, teaching you the concepts, the context, and the code. The entire book is drafted in Jupyter notebooks, seamlessly integrating exposition figures, math, and interactive examples with self-contained code. -
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Complete Machine Learning Package
A comprehensive machine learning repository containing 30+ notebooks
...The repository also includes examples related to natural language processing, computer vision, and data visualization, giving learners exposure to several subfields of machine learning. By organizing the content into modular notebooks, the project allows users to explore topics independently and experiment with the code directly. -
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Neural Network Intelligence
AutoML toolkit for automate machine learning lifecycle
Neural Network Intelligence is an open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning. NNI (Neural Network Intelligence) is a lightweight but powerful toolkit to help users automate feature engineering, neural architecture search, hyperparameter tuning and model compression. The tool manages automated machine learning (AutoML) experiments, dispatches and runs experiments'... -
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TurboPilot
Open source large-language-model based code completion engine
TurboPilot is a self-hosted copilot clone that uses the library behind llama.cpp to run the 6 Billion Parameter Salesforce Codegen model in 4GiB of RAM. It is heavily based and inspired by on the fauxpilot project. This is a proof of concept right now rather than a stable tool. Autocompletion is quite slow in this version of the project. Feel free to play with it, but your mileage may vary. -
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Lightning-Hydra-Template
PyTorch Lightning + Hydra. A very user-friendly template
...Not fitted for data engineering - the template configuration setup is not designed for building data processing pipelines that depend on each other. PyTorch Lightning, a lightweight PyTorch wrapper for high-performance AI research. Think of it as a framework for organizing your PyTorch code. Hydra, a framework for elegantly configuring complex applications. The key feature is the ability to dynamically create a hierarchical configuration by composition and override it through config files and the command line. -
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fe4ml-zh
Feature Engineering for Machine Learning
...The repository aims to make advanced feature engineering concepts accessible to a broader audience by translating the content and organizing it into readable documentation and code examples. Feature engineering is a critical component of machine learning pipelines because it determines how raw data is transformed into features that algorithms can use effectively. The project explains techniques for creating, selecting, and transforming features in ways that improve model accuracy and robustness. It also discusses the role of domain knowledge, data preprocessing, and statistical reasoning in building effective machine learning models. -
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OpenNMT-tf
Neural machine translation and sequence learning using TensorFlow
...While neural machine translation is the main target task, it has been designed to more generally support sequence-to-sequence mapping, sequence tagging, sequence classification, language modeling. Models are described with code to allow training custom architectures and overriding default behavior. For example, the following instance defines a sequence-to-sequence model with 2 concatenated input features, a self-attentional encoder, and an attentional RNN decoder sharing its input and output embeddings. Sequence to sequence models can be trained with guided alignment and alignment information are returned as part of the translation API. -
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FlubuCore
A cross platform build and deployment automation system
"FlubuCore - Fluent Builder Core" is a cross-platform build and deployment automation system. You can define your build and deployment scripts in C# using an intuitive fluent interface. This gives you code completion, IntelliSense, debugging, FlubuCore custom analyzers, and native access to the whole .NET ecosystem inside of your scripts. FlubuCore offers a .net (core) console application that uses power of roslyn to compile and execute scripts. Intuitive and easy to learn. C#, fluent interface, and IntelliSense make even the most complex script creation a breeze. ... -
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Horovod
Distributed training framework for TensorFlow, Keras, PyTorch, etc.
...Start scaling your model training with just a few lines of Python code. Scale up to hundreds of GPUs with upwards of 90% scaling efficiency. -
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pyts
A Python package for time series classification
...It aims to make time series classification easily accessible by providing preprocessing and utility tools, and implementations of several time series classification algorithms. The package comes up with many unit tests and continuous integration ensures new code integration and backward compatibility. The package is distributed under the 3-clause BSD license. -
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TensorFlow Documentation
TensorFlow documentation
An end-to-end platform for machine learning. TensorFlow makes it easy to create ML models that can run in any environment. Learn how to use the intuitive APIs through interactive code samples.
