Search Results for "claw-code" - Page 3
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Showing 366 open source projects for "claw-code"
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CARLA Simulator
Open-source simulator for autonomous driving research.
CARLA has been developed from the ground up to support development, training, and validation of autonomous driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites, environmental conditions, full control of all static and dynamic actors, maps generation and much more. Multiple clients in the same or in different nodes can control different actors. ... -
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Ray
A unified framework for scalable computing
Modern workloads like deep learning and hyperparameter tuning are compute-intensive and require distributed or parallel execution. Ray makes it effortless to parallelize single machine code — go from a single CPU to multi-core, multi-GPU or multi-node with minimal code changes. Accelerate your PyTorch and Tensorflow workload with a more resource-efficient and flexible distributed execution framework powered by Ray. Accelerate your hyperparameter search workloads with Ray Tune. Find the best model and reduce training costs by using the latest optimization algorithms. ... -
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Zero to Mastery Machine Learning
All course materials for the Zero to Mastery Machine Learning
...The project provides a structured curriculum designed to teach machine learning and data science using Python through hands-on projects and interactive notebooks. The repository includes datasets, Jupyter notebooks, documentation, and example code that walk learners through the entire machine learning workflow from problem definition to model deployment. The course introduces essential tools such as NumPy, pandas, Matplotlib, and scikit-learn before moving on to deep learning with frameworks like TensorFlow and Keras. It also includes milestone projects that demonstrate how to build end-to-end machine learning systems using real datasets, including classification and regression tasks. -
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DeepCamera
Open-Source AI Camera. Empower any camera/CCTV
...SharpAI-hub is the cloud hosting for AI applications that helps you deploy AI applications with your CCTV camera on your edge device in minutes. SharpAI yolov7_reid is an open-source Python application that leverages AI technologies to detect intruders with traditional surveillance cameras. The source code is here It leverages Yolov7 as a person detector, FastReID for person feature extraction, Milvus the local vector database for self-supervised learning to identify unseen persons, Labelstudio to host images locally and for further usage such as label data and train your own classifier. It also integrates with Home-Assistant to empower smart homes with AI technology. -
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Metarank
A low code Machine Learning service that personalizes articles
Metarank is a service that can personalize any type of content: product listings, articles, recommendations and search results in 3 easy steps with a few lines of code. It’s often considered "too risky" to spend 6+ months on an in-house moonshot project to reinvent the wheel without an experienced team and no existing open-source tools. Metarank makes it easy not only for Amazon to do personalization but for everyone else. Ingest historical item listings, clicks and item metadata so Metarank can find hidden dependencies in the data using our simple JSON format.No Machine Learning experience is required, run our CLI tool with a set of features in a YAML configuration. ... -
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.NET for Apache Spark
A free, open-source, and cross-platform big data analytics framework
...With these .NET APIs, you can access the most popular Dataframe and SparkSQL aspects of Apache Spark, for working with structured data, and Spark Structured Streaming, for working with streaming data. .NET for Apache Spark is compliant with .NET Standard - a formal specification of .NET APIs that are common across .NET implementations. This means you can use .NET for Apache Spark anywhere you write .NET code allowing you to reuse all the knowledge, skills, code, and libraries you already have as a .NET developer. .NET for Apache Spark runs on Windows, Linux, and macOS using .NET Core, or Windows using .NET Framework. It also runs on all major cloud providers including Azure HDInsight Spark, Amazon EMR Spark, AWS & Azure Databricks. -
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PML
The easiest way to use deep metric learning in your application
This library contains 9 modules, each of which can be used independently within your existing codebase, or combined together for a complete train/test workflow. To compute the loss in your training loop, pass in the embeddings computed by your model, and the corresponding labels. The embeddings should have size (N, embedding_size), and the labels should have size (N), where N is the batch size. The TripletMarginLoss computes all possible triplets within the batch, based on the labels you... -
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Watermark-Removal
Machine learning image inpainting task that removes watermarks
...Through these techniques, the model learns to identify regions of the image affected by the watermark and generate realistic replacements for the missing visual information. The repository contains code for preprocessing images, training the model, and running inference on images to automatically remove watermark artifacts. -
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RL with PyTorch
Clean, Robust, and Unified PyTorch implementation
...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 approaches. The repository is structured so that users can easily experiment with different algorithms and training environments. Many examples demonstrate how agents learn to interact with simulated environments through trial and error using reinforcement learning principles. ... -
AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
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Data Science Articles from CodeCut
Collection of useful data science topics along with articles
...The materials address areas such as MLOps, data management, project organization, testing practices, visualization techniques, and productivity tools used by data scientists. Each topic often includes references to code repositories, demonstrations, and video tutorials that show how the tools can be applied in real projects. The repository is intended to help practitioners stay updated with current best practices and technologies in the field of data science. -
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Optax
Optax is a gradient processing and optimization library for JAX
...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 readability and structuring code to match standard equations, over code reuse. -
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Fairlearn
A Python package to assess and improve fairness of ML models
Fairlearn is a Python package that empowers developers of artificial intelligence (AI) systems to assess their system's fairness and mitigate any observed unfairness issues. Fairlearn contains mitigation algorithms as well as metrics for model assessment. Besides the source code, this repository also contains Jupyter notebooks with examples of Fairlearn usage. An AI system can behave unfairly for a variety of reasons. In Fairlearn, we define whether an AI system is behaving unfairly in terms of its impact on people – i.e., in terms of harm. Fairness of AI systems is about more than simply running lines of code. ... -
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FLAML
A fast library for AutoML and tuning
...Users can find their desired customizability from a smooth range: minimal customization (computational resource budget), medium customization (e.g., scikit-style learner, search space, and metric), or full customization (arbitrary training and evaluation code). It supports fast automatic tuning, capable of handling complex constraints/guidance/early stopping. FLAML is powered by a new, cost-effective hyperparameter optimization and learner selection method invented by Microsoft Research. -
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Weights and Biases
Tool for visualizing and tracking your machine learning experiments
...Spend less time manually tracking results in spreadsheets and text files. Capture dataset versions with W&B Artifacts to identify how changing data affects your resulting models. Reproduce any model, with saved code, hyperparameters, launch commands, input data, and resulting model weights. Set wandb.config once at the beginning of your script to save your hyperparameters, input settings (like dataset name or model type), and any other independent variables for your experiments. This is useful for analyzing your experiments and reproducing your work in the future. ... -
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Gradio
Create UIs for your machine learning model in Python in 3 minutes
Gradio is the fastest way to demo your machine learning model with a friendly web interface so that anyone can use it, anywhere! Gradio can be installed with pip. Creating a Gradio interface only requires adding a couple lines of code to your project. You can choose from a variety of interface types to interface your function. Gradio can be embedded in Python notebooks or presented as a webpage. A Gradio interface can automatically generate a public link you can share with colleagues that lets them interact with the model on your computer remotely from their own devices. ... -
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python-small-examples
Focus on creating classic Python small examples and cases
python-small-examples is an open-source educational repository that contains hundreds of concise Python programming examples designed to illustrate practical coding techniques. The project focuses on teaching programming concepts through small, focused scripts that demonstrate common tasks in data processing, visualization, and general programming. Each example highlights a specific function or programming pattern so that learners can quickly understand how to apply Python features in... -
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Denoising Diffusion Probabilistic Model
Implementation of Denoising Diffusion Probabilistic Model in Pytorch
Implementation of Denoising Diffusion Probabilistic Model in Pytorch. It is a new approach to generative modeling that may have the potential to rival GANs. It uses denoising score matching to estimate the gradient of the data distribution, followed by Langevin sampling to sample from the true distribution. If you simply want to pass in a folder name and the desired image dimensions, you can use the Trainer class to easily train a model. -
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NeuroMatch Academy (NMA)
NMA Computational Neuroscience course
...These videos are completely optional and do not need to be watched in a fixed order so you can pick and choose which videos will help you brush up on your knowledge. The pre-reqs refresher days are asynchronous, so you can go through the material on your own time. You will learn how to code in Python from scratch using a simple neural model, the leaky integrate-and-fire model, as a motivation. Then, you will cover linear algebra, calculus and probability & statistics. The topics covered on these days were carefully chosen based on what you need for the comp neuro course. -
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machine-learning-refined
Master the fundamentals of machine learning, deep learning
...The project accompanies a series of textbooks and teaching materials that focus on making machine learning concepts accessible through visual demonstrations and simple code implementations. Instead of presenting algorithms purely through mathematical derivations, the repository emphasizes geometric intuition, visualization, and step-by-step experimentation. It includes Jupyter notebooks and scripts that illustrate core machine learning topics such as regression, classification, optimization methods, and neural networks. ... -
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Deepnote
Deepnote is a drop-in replacement for Jupyter
Deepnote is an open-source collaborative data science notebook platform designed as a modern alternative to traditional Jupyter notebooks. The project provides an AI-first computational environment where users can write, analyze, and share code, data, and visualizations in a single integrated workspace. Built on top of the Jupyter kernel ecosystem, it maintains compatibility with existing notebook workflows while introducing additional features focused on collaboration and automation. The system supports programming languages such as Python, R, and SQL and allows users to execute and analyze data directly within interactive notebooks. ... -
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Machine Learning Foundations
Machine Learning Foundations: Linear Algebra, Calculus, Statistics
Machine Learning Foundations repository contains the code, notebooks, and teaching materials used in Jon Krohn’s Machine Learning Foundations curriculum. The project focuses on explaining the fundamental mathematical and computational concepts that underpin modern machine learning and artificial intelligence systems. The materials cover essential topics such as linear algebra, calculus, statistics, and probability, which form the theoretical basis of many machine learning algorithms. ... -
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handson-ml
Teaching you the fundamentals of Machine Learning in python
...The examples underscore fundamentals like bias-variance trade-offs, regularization, and proper validation, grounding learners before they move to deep nets. Even though the deep learning stack evolved, the classical ML sections remain highly relevant for production data problems. The code is crafted to be clear rather than clever, prioritizing readability for newcomers. As a historical snapshot and a still-useful primer, it pairs well with the second edition for understanding how the ecosystem matured. -
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Ludwig
A codeless platform to train and test deep learning models
Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code. All you need to provide is a CSV file containing your data, a list of columns to use as inputs, and a list of columns to use as outputs, Ludwig will do the rest. Simple commands can be used to train models both locally and in a distributed way, and to use them to predict on new data. -
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DocTR
Library for OCR-related tasks powered by Deep Learning
...Seemlessly process documents for Natural Language Understanding tasks: we provide OCR predictors to parse textual information (localize and identify each word) from your documents. Robust 2-stage (detection + recognition) OCR predictors with pretrained parameters. User-friendly, 3 lines of code to load a document and extract text with a predictor. State-of-the-art performances on public document datasets, comparable with GoogleVision/AWS Textract. Easy integration (available templates for browser demo & API deployment). End-to-End OCR is achieved in docTR using a two-stage approach: text detection (localizing words), then text recognition (identify all characters in the word). ... -
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Scholar
Traditional machine learning on top of Nx
Traditional machine learning tools built on top of Nx. Scholar implements several algorithms for classification, regression, clustering, dimensionality reduction, metrics, and preprocessing.
