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Showing 11 open source projects for "error"
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Turn traffic into pipeline and prospects into customersDocket is an AI-powered sales enablement platform designed to unify go-to-market (GTM) data through its proprietary Sales Knowledge Lake™ and activate it with intelligent AI agents. The platform helps marketing teams increase pipeline generation by 15% by engaging website visitors in human-like conversations and qualifying leads. For sales teams, Docket improves seller efficiency by 33% by providing instant product knowledge, retrieving collateral, and creating personalized documents. Built for GTM teams, Docket integrates with over 100 tools across the revenue tech stack and offers enterprise-grade security with SOC 2 Type II, GDPR, and ISO 27001 compliance. Customers report improved win rates, shorter sales cycles, and dramatically reduced response times. Docket’s scalable, accurate, and fast AI agents deliver reliable answers with confidence scores, empowering teams to close deals faster.
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AestheticsPro Medical Spa SoftwareAestheticsPro is the most complete Aesthetics Software on the market today. HIPAA Cloud Compliant with electronic charting, integrated POS, targeted marketing and results driven reporting; AestheticsPro delivers the tools you need to manage your medical spa business. It is our mission To Provide an All-in-One Cutting Edge Software to the Aesthetics Industry.
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1
Thinc
A refreshing functional take on deep learning
...Develop faster and catch bugs sooner with sophisticated type checking. Trying to pass a 1-dimensional array into a model that expects two dimensions? That’s a type error. Your editor can pick it up as the code leaves your fingers. -
2
MONAI
AI Toolkit for Healthcare Imaging
...It is built on top of PyTorch and is released under the Apache 2.0 license. Aiming to capture best practices of AI development for healthcare researchers, with an immediate focus on medical imaging. Providing user-comprehensible error messages and easy to program API interfaces. Provides reproducibility of research experiments for comparisons against state-of-the-art implementations. -
3
marimo
A reactive notebook for Python
marimo is an open-source reactive notebook for Python, reproducible, git-friendly, executable as a script, and shareable as an app. marimo notebooks are reproducible, extremely interactive, designed for collaboration (git-friendly!), deployable as scripts or apps, and fit for modern Pythonista. Run one cell and marimo reacts by automatically running affected cells, eliminating the error-prone chore of managing the notebook state. marimo's reactive UI elements, like data frame GUIs and plots, make working with data feel refreshingly fast, futuristic, and intuitive. Version with git, run as Python scripts, import symbols from a notebook into other notebooks or Python files, and lint or format with your favorite tools. ... -
4
autoresearch
AI agents autonomously run and improve ML experiments overnight
...Designed to run on a single GPU, it keeps the research loop minimal and self-contained to make autonomous experimentation practical. Over time, the agent logs experiments, evaluates improvements, and gradually evolves the model through automated trial-and-error. -
Simplify Purchasing For Your BusinessSimplify every aspect of buying for your business in Order.co. From sourcing products to scaling purchasing across locations to automating your AP and approvals workstreams, Order.co is the platform of choice for growing businesses.
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5
RL with PyTorch
Clean, Robust, and Unified PyTorch implementation
...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. The codebase emphasizes clarity and modular design so that researchers can extend the implementations or use them for experimentation and benchmarking. -
6
Koila
Prevent PyTorch's `CUDA error: out of memory` in just 1 line of code
Koila is a lightweight Python library designed to help developers avoid memory errors when training deep learning models with PyTorch. The library introduces a lazy evaluation mechanism that delays computation until it is actually required, allowing the framework to better estimate the memory requirements of a model before execution. By building a computational graph first and executing operations only when necessary, koila reduces the risk of running out of GPU memory during the forward... -
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Homemade Machine Learning
Python examples of popular machine learning algorithms
homemade-machine-learning is a repository by Oleksii Trekhleb containing Python implementations of classic machine-learning algorithms done “from scratch”, meaning you don’t rely heavily on high-level libraries but instead write the logic yourself to deepen understanding. Each algorithm is accompanied by mathematical explanations, visualizations (often via Jupyter notebooks), and interactive demos so you can tweak parameters, data, and observe outcomes in real time. The purpose is... -
8
SAHI
A lightweight vision library for performing large object detection
A lightweight vision library for performing large-scale object detection & instance segmentation. Object detection and instance segmentation are by far the most important fields of applications in Computer Vision. However, detection of small objects and inference on large images are still major issues in practical usage. Here comes the SAHI to help developers overcome these real-world problems with many vision utilities. Detection of small objects and objects far away in the scene is a major... -
9
Spinning Up in Deep RL
Educational resource to help anyone learn deep reinforcement learning
...This is an educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning (deep RL). For the unfamiliar, reinforcement learning (RL) is a machine learning approach for teaching agents how to solve tasks by trial and error. Deep RL refers to the combination of RL with deep learning. At OpenAI, we believe that deep learning generally, and deep reinforcement learning specifically, will play central roles in the development of powerful AI technology. To ensure that AI is safe, we have to come up with safety strategies and algorithms that are compatible with this paradigm. ... -
The Most Powerful Software Platform for EHSQ and ESG ManagementChoose from a complete set of software solutions across EHSQ that address all aspects of top performing Environmental, Health and Safety, and Quality management programs.
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10
FID score for PyTorch
Compute FID scores with PyTorch
...FID is calculated by computing the Fréchet distance between two Gaussians fitted to feature representations of the Inception network. The weights and the model are exactly the same as in the official Tensorflow implementation, and were tested to give very similar results (e.g. .08 absolute error and 0.0009 relative error on LSUN, using ProGAN generated images). However, due to differences in the image interpolation implementation and library backends, FID results still differ slightly from the original implementation. In difference to the official implementation, you can choose to use a different feature layer of the Inception network instead of the default pool3 layer. -
11
Botnet Detectors Comparer
Compares botnet detection methods
Compares botnet detection methods by computing the error metrics by reading the labels on a NetFlow file. The original NetFlow should have a new column for the ground-truth label, and a new column with the prediction label for each botnet detection method. This program computes all the error metrics (TPR, TNR, FPR, FNR, Precision, Accuracy, ErrorRate, FMeasure1, FMeasure2, FMeasure0.5) and output the comparison results.
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