Search Results for "tutorials" - Page 5
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Showing 171 open source projects for "tutorials"
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Network Discovery Software | JDisc DiscoveryJDisc Discovery is a comprehensive network inventory and IT asset management solution designed to help organizations gain clear, up-to-date visibility into their IT environment. It automatically scans and maps devices across the network, including servers, workstations, virtual machines, and network hardware, to create a detailed inventory of all connected assets. This includes critical information such as hardware configurations, software installations, patch levels, and relationshipots between devices.
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Create engaging surveys on your tablet or computer with ease.Create great-looking surveys, forms, polls, voting, questionnaires, NPS, customer satisfaction, customer experience, employee satisfaction surveys... on your computer or tablet, customize the look of your survey however you like, & display collected data with eye-catching and insightful graphics.
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AI System & AI Infra
Tutorial repository focused on the full-stack design of AI systems
AI System is an educational and technical tutorial repository focused on the full-stack design of artificial intelligence systems, covering the foundational infrastructure that powers modern deep learning workloads. The project explores the AI software and hardware stack end to end, including AI chips, AI compilers, inference engines, and training frameworks, helping learners understand how these components interact in real-world deployments. Rather than being a single library, it functions... -
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ReplitLM
Inference code and configs for the ReplitLM model family
...These models are designed specifically for coding workflows and are trained on large datasets of source code covering many programming languages and development environments. The repository also includes documentation and tutorials for integrating the models into development tools, APIs, or research pipelines. Developers can fine-tune the models using instruction-tuning techniques to adapt them for specific programming tasks or domains. The models were trained using modern deep learning techniques and large-scale GPU infrastructure to achieve strong performance in code completion and generation tasks. -
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DeepKE
An Open Toolkit for Knowledge Graph Extraction and Construction
Supporting cnSchema, standard supervised setting, low-resource setting, document-level setting and multi-modal setting for knowledge base population. DeepKE is a knowledge extraction toolkit supporting cnSchema, standard supervised, low-resource, and document-level scenarios for entity, relation, and attribution extraction. It allows developers and researchers to customize datasets and models to extract information from unstructured texts. DeepKE supports low-resource settings with only a... -
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LLM Cookbook
LLM Introduction Tutorial for Developers, Chinese version
LLM Cookbook is an open-source learning repository designed to help developers understand how to build applications powered by large language models through practical examples and translated course material. The project adapts and reproduces content from widely known LLM developer courses and reorganizes it into a structured learning path tailored for developers who want to build real AI applications. It covers the essential topics required to start working with LLM APIs and frameworks,... -
Shoplogix Smart Factory PlatformReal-time Visibility into Your Shop Floor's Performance. The Shoplogix smart factory platform enables manufacturers to increase overall equipment effectiveness, reduce operational costs, sustain growth and improve profitability by allowing them to visualize, integrate and act on production and machine performance in real-time. Manufacturers that trust us to drive efficiency in their factories. Real-time visual data and analytics provide valuable insights to make better informed decisions. Uncover hidden shop floor potential and drive rapid time to value. Develop a continuously improving culture through training, education and data-driven decisions. Compete in the i4.0 world by making the Shoplogix Smart Factory Platform the cornerstone of your digital transformation. Connect to any equipment or device to automate data collection and exchange it with other manufacturing technologies. Automatically monitor, report and analyze machine states to track real-time production.
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EasyRL
Reinforcement learning (RL) tutorial series
easy-rl is a beginner-friendly reinforcement learning (RL) tutorial series and framework developed by Datawhale China. It provides educational resources and implementations of various RL algorithms to help new researchers and practitioners learn RL concepts. -
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AI Explainability 360
Interpretability and explainability of data and machine learning model
...The AI Explainability 360 interactive experience provides a gentle introduction to the concepts and capabilities by walking through an example use case for different consumer personas. The tutorials and example notebooks offer a deeper, data scientist-oriented introduction. The complete API is also available. There is no single approach to explainability that works best. There are many ways to explain: data vs. model, directly interpretable vs. post hoc explanation, local vs. global, etc. It may therefore be confusing to figure out which algorithms are most appropriate for a given use case. -
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Prime QA
State-of-the-art Multilingual Question Answering research
PrimeQA is a public open source repository that enables researchers and developers to train state-of-the-art models for question answering (QA). By using PrimeQA, a researcher can replicate the experiments outlined in a paper published in the latest NLP conference while also enjoying the capability to download pre-trained models (from an online repository) and run them on their own custom data. PrimeQA is built on top of the Transformers toolkit and uses datasets and models that are directly... -
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From Zero to Research Scientist guide
Detailed and tailored guide for undergraduate students
...The repository focuses primarily on deep learning and natural language processing, providing structured guidance for individuals who want to pursue research careers in these fields. It compiles recommended courses, textbooks, tutorials, and academic resources needed to build expertise in machine learning research. The guide proposes different learning paths depending on whether the learner prefers a theoretical approach centered on mathematics or a practical approach based on hands-on experimentation. It also introduces key research areas and topics that students should explore in order to understand modern AI research directions. -
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KotlinDL
High-level Deep Learning Framework written in Kotlin
KotlinDL is a high-level Deep Learning API written in Kotlin and inspired by Keras. Under the hood, it uses TensorFlow Java API and ONNX Runtime API for Java. KotlinDL offers simple APIs for training deep learning models from scratch, importing existing Keras and ONNX models for inference, and leveraging transfer learning for tailoring existing pre-trained models to your tasks. This project aims to make Deep Learning easier for JVM and Android developers and simplify deploying deep learning... -
Reliable Phone Service for Your Home or BusinessCalling made modern. Your business number. Your employees' phones. Our amazing features. A dial menu spoken by our voice actors. Callers press numbers to make purchases, hear MP3s, connect to specific staff, and more. Make and answer calls using your number on multiple phones without the caller ever knowing. Employees hear secret in-house menus, transfer calls, and send voicemails to their email, all from their dialpad. These business features require no new software or hardware. Your dialpad come to life. Porting your business or personal number at the press of a button. Select from our menu of modern voice features for your business or personal line. We'll activate these features on your current phone for you. No work (or learning) required from you. We'll be here to transform your number whenever your desires change.
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LightFM
A Python implementation of LightFM, a hybrid recommendation algorithm
LightFM is a Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback, including efficient implementation of BPR and WARP ranking losses. It's easy to use, fast (via multithreaded model estimation), and produces high-quality results. It also makes it possible to incorporate both item and user metadata into the traditional matrix factorization algorithms. It represents each user and item as the sum of the latent representations of their... -
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stable-diffusion-webui-colab
Stable diffusion webui colab
Stable Diffusion webui colab. lite has a stable WebUI and stable installed extensions. stable has ControlNet, a stable WebUI, and stable installed extensions. Nightly has ControlNet, the latest WebUI, and daily installed extension updates. If you want to use more models, you can download your model into Colab, which has an empty 50GB space. You can also free up more space by deleting the default model in your drive. If you don't plan to use ControlNet models, you can also free up space by... -
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Understand Prompt
Structured exploration and summary of prompt engineering practices
Understand Prompt is a repository by Phodal Huang that serves as a structured exploration and summary of prompt engineering practices across coding, art, and writing contexts, especially in the era of AI models like StableDiffusion and ChatGPT. It’s part tutorial, part reflection: the author writes about how prompts work (for image generation, article generation, code auto-generation) and shares notebooks, examples, and insights into how to design effective prompts, iterate them, and... -
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chatgpt HTML
PHP version calls the OpenAI interface for question and answer
...The UI on the page is concise and supports continuous conversations in the context. The source code has only a few files, no frame is used, all PHP versions are supported, all open source, and it is extremely easy to open. Nanny-level tutorials, all surrounding resources, welcome to communicate, everything is free. There is no requirement for the PHP version, no database is required. The core code has only a few files, and no frame is used. It is convenient to modify the debugging. It only needs to modify the API_KEY in stream.php to use it. -
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OpenNN - Open Neural Networks Library
Machine learning algorithms for advanced analytics
...This library outstands in terms of execution speed and memory allocation. It is constantly optimized and parallelized in order to maximize its efficiency. The documentation is composed by tutorials and examples to offer a complete overview about the library. OpenNN is developed by Artelnics, a company specialized in artificial intelligence. -
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ImageAI
A python library built to empower developers
ImageAI is an easy-to-use Computer Vision Python library that empowers developers to easily integrate state-of-the-art Artificial Intelligence features into their new and existing applications and systems. It is used by thousands of developers, students, researchers, tutors and experts in corporate organizations around the world. You will find features supported, links to official documentation as well as articles on ImageAI. ImageAI is widely used around the world by professionals,... -
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Machine Learning Git Codebook
For extensive instructor led learning
Machine Learning Git Codebook is an educational repository that provides a structured introduction to data science and machine learning concepts through a series of interactive notebooks and practical examples. The project is designed as a self-paced learning resource that walks learners through the full data science workflow, including data preprocessing, exploratory analysis, feature engineering, and model development. It covers a wide range of machine learning techniques such as decision... -
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learn-machine-learning-in-two-months
Essential Knowledge for learning Machine Learning in two months
The learn-machine-learning-in-two-months repository is an educational open-source project designed to guide beginners through the process of learning machine learning and deep learning concepts within a structured two-month study plan. The project compiles curated resources, tutorials, and practical notebooks that introduce fundamental topics such as mathematics for machine learning, Python programming, and essential libraries like NumPy and TensorFlow. It progressively moves from foundational theory to more advanced subjects including regression, classification, neural networks, and model deployment. The repository emphasizes understanding the underlying principles of machine learning while also providing practical exercises and examples that allow learners to build and experiment with real models. ... -
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Data Science Collected Resources
Carefully curated resource links for data science in one place
Data Science Collected Resources is a curated collection of learning materials and reference links covering a wide range of topics in data science, artificial intelligence, and machine learning. The repository aggregates educational resources from research articles, technical blogs, tutorials, and documentation into a single organized knowledge hub. Its goal is to provide learners and practitioners with easy access to high-quality resources related to data science tools, programming languages, cloud platforms, and machine learning techniques. The repository includes links to materials discussing topics such as artificial intelligence research, AWS infrastructure, machine learning algorithms, and data analysis tools. ... -
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Python ML Jupyter Notebooks
Practice and tutorial-style notebooks
Python ML Jupyter Notebooks is an educational repository that demonstrates how to implement machine learning algorithms and data science workflows using Python. The project provides numerous examples and tutorials covering classical machine learning techniques such as regression, classification, clustering, and dimensionality reduction. It includes code implementations that show how to build models using popular libraries like scikit-learn, NumPy, pandas, and Matplotlib. The repository is designed to help learners understand both the theory and practical implementation of machine learning algorithms through step-by-step code examples. ... -
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3D-Machine-Learning
A resource repository for 3D machine learning
3D-Machine-Learning is an open-source repository that compiles resources related to machine learning techniques applied to three-dimensional data. The project acts as a curated research directory that includes papers, datasets, tutorials, and software tools relevant to the emerging field of 3D machine learning. This interdisciplinary domain combines ideas from computer vision, computer graphics, and deep learning to analyze and generate three-dimensional structures. The repository includes references to important research papers covering topics such as point cloud processing, 3D reconstruction, shape analysis, and scene understanding. ... -
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DeepLearning Tutorial
Deep Learning Tutorial, Excellent Articles, Deep Learning Tutorial
...The repository organizes these materials into structured tutorials and references that allow readers to explore deep learning concepts progressively. Many of the resources include explanations of common model architectures used in computer vision and artificial intelligence. The project also collects recommended articles and educational documents that expand on deep learning theory and practical implementation strategies. -
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PyTorch Handbook
The pytorch handbook is an open source book
PyTorch Handbook is an open-source educational project designed to help developers and researchers quickly learn deep learning using the PyTorch framework. The repository functions as an online handbook that explains how to build, train, and evaluate neural network models using PyTorch. It includes tutorials and examples that demonstrate common deep learning tasks such as image classification, neural network design, model training workflows, and evaluation techniques. 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. ... -
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Guia do Cientista de Dados das Galáxias
Repository for gathering information on study materials
...The project was created by the Pizza de Dados community with the goal of organizing useful materials for people interested in learning or working in the data science ecosystem. The repository collects links to books, podcasts, tutorials, datasets, communities, and study groups that can help learners navigate the field of data science more efficiently. Instead of focusing on a single software framework, the project functions as a curated knowledge hub where contributors organize resources into thematic categories such as visualization, machine learning, programming languages, and analytics methodologies. ... -
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YOLOv3
Object detection architectures and models pretrained on the COCO data
...Students love YOLOv5 for its simplicity and there are many quickstart examples for you to get started within seconds. 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 -
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Hugging Face Transformer
CPU/GPU inference server for Hugging Face transformer models
...At Lefebvre Dalloz we run in-production semantic search engines in the legal domain, in the non-marketing language it's a re-ranker, and we based ours on Transformer. In that setup, latency is key to providing a good user experience, and relevancy inference is done online for hundreds of snippets per user query. Most tutorials on Transformer deployment in production are built over Pytorch and FastAPI. Both are great tools but not very performant in inference. Then, if you spend some time, you can build something over ONNX Runtime and Triton inference server. You will usually get from 2X to 4X faster inference compared to vanilla Pytorch. It's cool! However, if you want the best in class performances on GPU, there is only a single possible combination: Nvidia TensorRT and Triton. ...
