Search Results for "e-learning" - Page 8
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Showing 1155 open source projects for "e-learning"
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Jina-Serve
Build multimodal AI applications with cloud-native stack
...Jina Serve focuses on making it easier to turn machine learning models into production-ready services without forcing developers to manage complex infrastructure manually. The framework supports many major machine learning libraries and data types, making it suitable for multimodal AI systems that process text, images, audio, and other inputs. -
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MediaPipe Solutions
Cross-platform, customizable ML solutions
MediaPipe is an open-source framework developed by Google for building cross-platform machine learning pipelines that process audio, video, and other streaming data in real time. The system provides developers with tools and reusable components that allow them to combine multiple machine learning models with preprocessing and postprocessing logic into efficient perception pipelines. These pipelines can run on a wide variety of platforms including mobile devices, desktop systems, web browsers, and embedded edge devices. ... -
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handson-ml
Teaching you the fundamentals of Machine Learning in python
handson-ml hosts the notebooks for the first edition of the same hands-on ML book, reflecting the tooling and idioms of its time while teaching durable concepts. It walks through supervised and unsupervised learning with scikit-learn, then introduces deep learning using the earlier TensorFlow 1 graph-execution style. 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. ... -
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DeepCamera
Open-Source AI Camera. Empower any camera/CCTV
...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. -
Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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Hivemind
Decentralized deep learning in PyTorch. Built to train models
Hivemind is a PyTorch library for decentralized deep learning across the Internet. Its intended usage is training one large model on hundreds of computers from different universities, companies, and volunteers. Distributed training without a master node: Distributed Hash Table allows connecting computers in a decentralized network. Fault-tolerant backpropagation: forward and backward passes succeed even if some nodes are unresponsive or take too long to respond. -
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Colossal-AI
Making large AI models cheaper, faster and more accessible
...It remains a challenge for AI researchers to implement complex distributed training solutions for their models. Colossal-AI provides a collection of parallel components for you. We aim to support you to write your distributed deep learning models just like how you write your model on your laptop. -
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TorchCode
Practice implementing softmax, attention, GPT-2 and more
TorchCode is an interactive learning and practice platform designed to help developers master PyTorch by implementing core machine learning operations and architectures from scratch. It is structured similarly to competitive programming platforms like LeetCode but focuses specifically on tensor operations and deep learning concepts. The platform provides a collection of curated problems that cover fundamental topics such as activation functions, normalization layers, attention mechanisms, and full transformer architectures. ... -
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VisualDL
Deep Learning Visualization Toolkit
VisualDL, a visualization analysis tool of PaddlePaddle, provides a variety of charts to show the trends of parameters and visualizes model structures, data samples, histograms of tensors, PR curves , ROC curves and high-dimensional data distributions. It enables users to understand the training process and the model structure more clearly and intuitively so as to optimize models efficiently. VisualDL provides various visualization functions, including tracking metrics in real-time,... -
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SageMaker Training Toolkit
Train machine learning models within Docker containers
Train machine learning models within a Docker container using Amazon SageMaker. Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. -
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ChatterBot
Machine learning, conversational dialog engine for creating chat bots
...Additionally, the machine-learning nature of ChatterBot allows an agent instance to improve it’s own knowledge of possible responses as it interacts with humans and other sources of informative data. An untrained instance of ChatterBot starts off with no knowledge of how to communicate. Each time a user enters a statement, the library saves the text that they entered and the text that the statement was in response to. -
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Python Programming Hub
Learn Python and Machine Learning from scratch
...The project contains a wide range of tutorials and exercises that cover Python fundamentals, programming concepts, and applied techniques for data analysis and machine learning. Many sections are implemented as Jupyter notebooks, allowing learners to run code interactively while reading explanations of the concepts involved. The repository emphasizes hands-on learning by demonstrating real programming tasks such as data manipulation, statistical analysis, visualization, and automation. It also includes examples of commonly used libraries such as NumPy, Pandas, and other tools used in data science workflows. -
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C3
The goal of CLAIMED is to enable low-code/no-code rapid prototyping
C3 is an open-source framework designed to simplify the development and deployment of data science and machine learning workflows through reusable components and low-code development techniques. The framework focuses on enabling rapid prototyping while maintaining a path to production through automated CI/CD integration. CLAIMED provides a component-based architecture where data processing steps, models, and workflows can be packaged into reusable operators. -
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Katib
Automated Machine Learning on Kubernetes
Katib is a Kubernetes-native project for automated machine learning (AutoML). Katib supports Hyperparameter Tuning, Early Stopping and Neural Architecture Search. Katib is a project that is agnostic to machine learning (ML) frameworks. It can tune hyperparameters of applications written in any language of the users’ choice and natively supports many ML frameworks, such as TensorFlow, Apache MXNet, PyTorch, XGBoost, and others. -
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Final2x
2^x Image Super-Resolution
The tool is available for Windows x64/arm64, MacOS x64/arm64, and Linux x64, allowing users to enjoy the benefits of super-resolution regardless of their operating system. It offers a wide range of models that can be used to achieve different levels of super-resolution, allowing users to choose the one that best suits their specific needs. Users have the flexibility to specify the desired output size for their images, ranging from small enhancements to large-scale super-resolution. The tool... -
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Responsible AI Toolbox
Responsible AI Toolbox is a suite of tools providing model
Responsible AI Toolbox is a software framework designed to help developers evaluate and improve the reliability, fairness, and transparency of machine learning systems. The project provides tools that assist in analyzing model behavior, detecting bias, improving robustness, and explaining predictions produced by AI systems. It is designed to integrate with common machine learning frameworks, especially PyTorch, allowing developers to apply responsible AI techniques within existing workflows. ... -
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hls4ml
Machine learning on FPGAs using HLS
hls4ml is an open-source framework that enables machine learning models to be implemented directly on hardware such as FPGAs and ASICs using high-level synthesis techniques. The system converts trained neural network models from common machine learning frameworks into hardware description code suitable for ultra-low-latency inference. This approach allows machine learning algorithms to run directly on specialized hardware, making them suitable for applications that require extremely fast response times and minimal power consumption. ... -
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deepjazz
Deep learning driven jazz generation using Keras & Theano
deepjazz is a deep learning project that generates jazz music using recurrent neural networks trained on MIDI files. The repository demonstrates how machine learning can learn musical structure and produce original compositions. It uses the Keras and Theano libraries to build a two-layer Long Short-Term Memory network capable of learning temporal patterns in music. -
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ProjectLearn.io
A curated list of project tutorials for project-based learning
ProjectLearn.io is an open-source repository that aggregates curated tutorials focused on project-based programming education. The project organizes learning resources where users build complete applications from scratch, helping learners acquire practical development experience rather than relying solely on theoretical tutorials. The repository includes projects across multiple domains such as web development, mobile development, machine learning, artificial intelligence, and game development. ... -
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DeepCTR-Torch
Easy-to-use,Modular and Extendible package of deep-learning models
DeepCTR-Torch is an easy-to-use, Modular and Extendible package of deep-learning-based CTR models along with lots of core components layers that can be used to build your own custom model easily.It is compatible with PyTorch.You can use any complex model with model.fit() and model.predict(). With the great success of deep learning, DNN-based techniques have been widely used in CTR estimation tasks. The data in the CTR estimation task usually includes high sparse,high cardinality categorical features and some dense numerical features. ... -
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Lightly
A python library for self-supervised learning on images
A python library for self-supervised learning on images. We, at Lightly, are passionate engineers who want to make deep learning more efficient. That's why - together with our community - we want to popularize the use of self-supervised methods to understand and curate raw image data. Our solution can be applied before any data annotation step and the learned representations can be used to visualize and analyze datasets. -
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fastai
Deep learning library
fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches. It aims to do both things without substantial compromises in ease of use, flexibility, or performance. -
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BudouX
Standalone, small, language-neutral
Standalone. Small. Language-neutral. BudouX is the successor to Budou, the machine learning-powered line break organizer tool. It is standalone. It works with no dependency on third-party word segmenters such as Google cloud natural language API. It is small. It takes only around 15 KB including its machine learning model. It's reasonable to use it even on the client-side. It is language-neutral. You can train a model for any language by feeding a dataset to BudouX’s training script. -
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Kubeflow Training Operator
Distributed ML Training and Fine-Tuning on Kubernetes
Kubeflow Training Operator is a Kubernetes-native project for fine-tuning and scalable distributed training of machine learning (ML) models created with various ML frameworks such as PyTorch, TensorFlow, XGBoost, MPI, Paddle, and others. -
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Turing.jl
Bayesian inference with probabilistic programming
Bayesian inference with probabilistic programming. -
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PyBroker
Algorithmic Trading in Python with Machine Learning
Are you looking to enhance your trading strategies with the power of Python and machine learning? Then you need to check out PyBroker! This Python framework is designed for developing algorithmic trading strategies, with a focus on strategies that use machine learning. With PyBroker, you can easily create and fine-tune trading rules, build powerful models, and gain valuable insights into your strategy’s performance.
