Search Results for "machine learning projects" - Page 29
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Showing 1162 open source projects for "machine learning projects"
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Machine Learning Beginner
Machine Learning Beginner Public Account Works
Machine Learning Beginner targets newcomers who are just getting started with machine learning and need a gentle, guided path. It introduces the core vocabulary and the mental map of supervised and unsupervised learning before moving into simple algorithms. The materials prioritize conceptual clarity, then progressively add code to solidify understanding. -
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ML workspace
All-in-one web-based IDE specialized for machine learning
All-in-one web-based development environment for machine learning. The ML workspace is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on your own machines. This workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch, Keras, Sklearn) and dev tools (e.g., Jupyter, VS Code, Tensorboard) perfectly configured, optimized, and integrated. ... -
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Kashgari
Kashgari is a production-level NLP Transfer learning framework
Kashgari is a simple and powerful NLP Transfer learning framework, build a state-of-art model in 5 minutes for named entity recognition (NER), part-of-speech tagging (PoS), and text classification tasks. -
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tensorflow_template_application
TensorFlow template application for deep learning
...It is particularly useful for developers who want to transition from experimental notebooks to structured machine learning applications. By providing a reusable framework, the template reduces the time needed to set up new TensorFlow projects and encourages consistent development practices. -
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Machine-Learning-Notes
Zhou Zhihua's "Machine Learning" push notes
The Machine-Learning-Notes repository contains detailed handwritten-style study notes based on the popular machine learning textbook by Zhou Zhihua. The project focuses on deriving formulas and explaining algorithms step by step so that learners can understand the mathematical foundations behind machine learning methods. The notes span sixteen chapters that cover a wide range of topics, including model evaluation, linear models, decision trees, neural networks, support vector machines, Bayesian classifiers, ensemble methods, clustering, dimensionality reduction, and reinforcement learning. -
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Texthero
Text preprocessing, representation and visualization from zero to hero
Texthero is a python package to work with text data efficiently. It empowers NLP developers with a tool to quickly understand any text-based dataset and it provides a solid pipeline to clean and represent text data, from zero to hero. -
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FARM
Fast & easy transfer learning for NLP
FARM makes Transfer Learning with BERT & Co simple, fast and enterprise-ready. It's built upon transformers and provides additional features to simplify the life of developers: Parallelized preprocessing, highly modular design, multi-task learning, experiment tracking, easy debugging and close integration with AWS SageMaker. With FARM you can build fast proofs-of-concept for tasks like text classification, NER or question answering and transfer them easily into production. Easy fine-tuning... -
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YOLOv4-large
Scaled-YOLOv4: Scaling Cross Stage Partial Network
YOLOv4-large is an open-source implementation of the Scaled-YOLOv4 object detection architecture, designed to improve both the accuracy and scalability of real-time computer vision models. The project provides a PyTorch implementation of the Scaled-YOLOv4 framework, which extends the original YOLOv4 architecture using Cross Stage Partial (CSP) networks and new scaling techniques. Unlike earlier object detection systems that only scale depth or width, this architecture scales multiple aspects... -
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neurojs
A JavaScript deep learning and reinforcement learning library
neurojs is a JavaScript framework designed to enable deep learning and reinforcement learning directly within web environments. The library provides a full machine learning framework implemented in JavaScript that can run inside browsers or Node.js environments. It focuses particularly on reinforcement learning algorithms, enabling developers to create intelligent agents that learn through interaction with simulated environments. -
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course-v3
The 3rd edition of course.fast.ai
course-v3 repository contains the complete learning materials for the third edition of the Practical Deep Learning for Coders course developed by the fast.ai research group. The repository includes Jupyter notebooks, lesson materials, datasets, and supporting documentation used in the course to teach modern deep learning techniques. The course emphasizes a top-down approach to learning artificial intelligence, where students begin by building practical models and later study the underlying... -
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AliceMind
ALIbaba's Collection of Encoder-decoders from MinD
This repository provides pre-trained encoder-decoder models and its related optimization techniques developed by Alibaba's MinD (Machine IntelligeNce of Damo) Lab. Pre-trained models for natural language understanding (NLU). We extend BERT to a new model, StructBERT, by incorporating language structures into pre-training. Specifically, we pre-train StructBERT with two auxiliary tasks to make the most of the sequential order of words and sentences, which leverage language structures at the... -
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TensorFlow.js models
Pretrained models for TensorFlow.js
This repository hosts a set of pre-trained models that have been ported to TensorFlow.js. The models are hosted on NPM and unpkg so they can be used in any project out of the box. They can be used directly or used in a transfer learning setting with TensorFlow.js. To find out about APIs for models, look at the README in each of the respective directories. In general, we try to hide tensors so the API can be used by non-machine learning experts. New models should have a test NPM script. You can run the unit tests for any of the models by running "yarn test" inside a directory. ... -
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SRU
Training RNNs as Fast as CNNs
Common recurrent neural architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations. In this work, we propose the Simple Recurrent Unit (SRU), a light recurrent unit that balances model capacity and scalability. SRU is designed to provide expressive recurrence, enable highly parallelized implementation, and comes with careful initialization to facilitate the training of deep models. We demonstrate the effectiveness of SRU on multiple NLP tasks. SRU... -
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Minkowski Engine
Auto-diff neural network library for high-dimensional sparse tensors
The Minkowski Engine is an auto-differentiation library for sparse tensors. It supports all standard neural network layers such as convolution, pooling, unspooling, and broadcasting operations for sparse tensors. The Minkowski Engine supports various functions that can be built on a sparse tensor. We list a few popular network architectures and applications here. To run the examples, please install the package and run the command in the package root directory. Compressing a neural network to... -
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Opyrator
Turns your machine learning code into microservices with web API
Instantly turn your Python functions into production-ready microservices. Deploy and access your services via HTTP API or interactive UI. Seamlessly export your services into portable, shareable, and executable files or Docker images. Opyrator builds on open standards - OpenAPI, JSON Schema, and Python type hints - and is powered by FastAPI, Streamlit, and Pydantic. It cuts out all the pain for productizing and sharing your Python code - or anything you can wrap into a single Python... -
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Pwnagotchi
Deep Reinforcement learning instrumenting bettercap for WiFi pwning
Pwnagotchi is an A2C-based “AI” powered by bettercap and running on a Raspberry Pi Zero W that learns from its surrounding WiFi environment in order to maximize the crackable WPA key material it captures (either through passive sniffing or by performing deauthentication and association attacks). This material is collected on disk as PCAP files containing any form of handshake supported by hashcat, including full and half WPA handshakes as well as PMKIDs. Instead of merely playing Super Mario... -
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Couler
Unified Interface for Constructing and Managing Workflows
Couler is a system designed for unified machine learning workflow optimization in the cloud. Couler endeavors to provide a unified interface for constructing and optimizing workflows across various workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow. Couler enhances workflow efficiency through features like Autonomous Workflow Construction, Automatic Artifact Caching Mechanisms, Big Workflow Auto Parallelism Optimization, and Automatic Hyperparameters Tuning. -
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MLOps Course
Learn how to design, develop, deploy and iterate on ML apps
The MLOps Course by Goku Mohandas is an open-source curriculum that teaches how to combine machine learning with solid software engineering to build production-grade ML applications. It is structured around the full lifecycle: data pipelines, modeling, experiment tracking, deployment, testing, monitoring, and iteration. The repository itself contains configuration, code examples, and links to accompanying lessons hosted on the Made With ML site, which provide detailed narrative explanations and diagrams. ... -
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Swift for TensorFlow
Swift for TensorFlow
Swift for TensorFlow repository contains the open-source implementation of Swift for TensorFlow, a project that integrates machine learning capabilities directly into the Swift programming language. The initiative aims to provide a new programming model for developing machine learning systems by combining the power of TensorFlow with language-level features such as automatic differentiation and strong type systems. By embedding machine learning functionality into the Swift compiler and language design, the project enables developers to write high-performance machine learning models while maintaining the readability and safety of modern programming practices. ... -
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Bayesian machine learning notebooks
Notebooks about Bayesian methods for machine learning
Notebooks about Bayesian methods for machine learning. -
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Keepsake
Version control for machine learning
Keepsake is a Python library that uploads files and metadata (like hyperparameters) to Amazon S3 or Google Cloud Storage. You can get the data back out using the command-line interface or a notebook. -
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Semantic Segmentation in PyTorch
Semantic segmentation models, datasets & losses implemented in PyTorch
Semantic segmentation models, datasets and losses implemented in PyTorch. PyTorch and Torchvision needs to be installed before running the scripts, together with PIL and opencv for data-preprocessing and tqdm for showing the training progress. PyTorch v1.1 is supported (using the new supported tensoboard); can work with earlier versions, but instead of using tensoboard, use tensoboardX. Poly learning rate, where the learning rate is scaled down linearly from the starting value down to zero... -
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Lucid
A collection of infrastructure and tools for research
Lucid is a collection of infrastructure and tools for research in neural network interpretability. Lucid is research code, not production code. We provide no guarantee it will work for your use case. Lucid is maintained by volunteers who are unable to provide significant technical support. Start visualizing neural networks with no setup. The following notebooks run right from your browser, thanks to Collaboratory. It's a Jupyter notebook environment that requires no setup to use and runs... -
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CapsGNN
A PyTorch implementation of "Capsule Graph Neural Network"
A PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019). The high-quality node embeddings learned from the Graph Neural Networks (GNNs) have been applied to a wide range of node-based applications and some of them have achieved state-of-the-art (SOTA) performance. However, when applying node embeddings learned from GNNs to generate graph embeddings, the scalar node representation may not suffice to preserve the node/graph properties efficiently, resulting in sub-optimal graph... -
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YOLO ROS
YOLO ROS: Real-Time Object Detection for ROS
This is a ROS package developed for object detection in camera images. You only look once (YOLO) is a state-of-the-art, real-time object detection system. In the following ROS package, you are able to use YOLO (V3) on GPU and CPU. The pre-trained model of the convolutional neural network is able to detect pre-trained classes including the data set from VOC and COCO, or you can also create a network with your own detection objects. The YOLO packages have been tested under ROS Noetic and...
