Search Results for "can" - Page 16
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Showing 564 open source projects for "can"
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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. ... -
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neurojs
A JavaScript deep learning and reinforcement learning library
...Several interactive demonstrations included with the project illustrate how neural networks can be used to train agents in simulated tasks, including a browser-based self-driving car example. These demos allow users to visualize how reinforcement learning agents improve their behavior over time as they receive rewards and update their neural networks. -
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Opyrator
Turns your machine learning code into microservices with web API
...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 function. An Opyrator-compatible function is required to have an input parameter and return value based on Pydantic models. The input and output models are specified via type hints. You can launch a graphical user interface - powered by Streamlit - for your compatible function. The UI is auto-generated from the input- and output-schema of the given function. -
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Pytorch Points 3D
Pytorch framework for doing deep learning on point clouds
...The framework currently integrates some of the best-published architectures and it integrates the most common public datasets for ease of reproducibility. It heavily relies on Pytorch Geometric and Facebook Hydra library thanks for the great work! We aim to build a tool that can be used for benchmarking SOTA models, while also allowing practitioners to efficiently pursue research into point cloud analysis, with the end goal of building models which can be applied to real-life applications. Task driven implementation with dynamic model and dataset resolution from arguments. Core implementation of common components for point cloud deep learning - greatly simplifying the creation of new models. 4 Base Convolution base classes to simplify the implementation of new convolutions. ... -
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YOLO ROS
YOLO ROS: Real-Time Object Detection for ROS
...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 Ubuntu 20.04. We also provide branches that work under ROS Melodic, ROS Foxy and ROS2. Darknet on the CPU is fast (approximately 1.5 seconds on an Intel Core i7-6700HQ CPU @ 2.60GHz × 8) but it's like 500 times faster on GPU! ... -
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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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CapsGNN
A PyTorch implementation of "Capsule Graph Neural Network"
...Inspired by the Capsule Neural Network (CapsNet), we propose the Capsule Graph Neural Network (CapsGNN), which adopts the concept of capsules to address the weakness in existing GNN-based graph embeddings algorithms. By extracting node features in the form of capsules, routing mechanism can be utilized to capture important information at the graph level. As a result, our model generates multiple embeddings for each graph to capture graph properties from different aspects. -
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Machine-Learning-Notes
Zhou Zhihua's "Machine Learning" push notes
...Each section explains the theoretical principles of the algorithms and walks through derivations to help readers understand why the methods work rather than simply how to use them. The repository organizes the material into printable chapters so that students can study the notes offline or use them as reference material while learning machine learning theory. -
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Lucid
A collection of infrastructure and tools for research
...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 entirely in the cloud. You can run the notebooks on your local machine, too. Clone the repository and find them in the notebooks subfolder. You will need to run a local instance of the Jupyter notebook environment to execute them. Feature visualization answers questions about what a network, or parts of a network, are looking for by generating examples. -
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Semantic Segmentation in PyTorch
Semantic segmentation models, datasets & 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 during training. Considered as the go-to scheduler for semantic segmentation. One Cycle learning rate, for a learning rate LR, we start from LR / 10 up to LR for 30% of the training time, and we scale down to LR / 25 for remaining time, the scaling is done in a cos annealing fashion (see Figure bellow), the momentum is also modified but in the opposite manner starting from 0.95 down to 0.85 and up to 0.95. -
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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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Keras TCN
Keras Temporal Convolutional Network
...The receptive field of the TCN can be calculated. Once keras-tcn is installed as a package, you can take a glimpse of what is possible to do with TCNs. Some tasks examples are available in the repository for this purpose. -
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Universal Data Tool
Collaborate & label any type of data, images, text, or documents etc.
...Import from your S3 buckets easily with IAM or Cognito authentication. Working together, we can accomplish more. The Universal Data Tool was built to bring together the best ideas from different machine learning communities. Upload your dataset to Courses to create a training course. Testing and exercises validate that your workforce knows exactly how the data should be labeled. Get started in less than a minute. Courses uses administrator links. -
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BerryNet
Deep learning gateway on Raspberry Pi and other edge devices
This project turns edge devices such as Raspberry Pi into an intelligent gateway with deep learning running on it. No internet connection is required, everything is done locally on the edge device itself. Further, multiple edge devices can create a distributed AIoT network. At DT42, we believe that bringing deep learning to edge devices is the trend towards the future. It not only saves costs of data transmission and storage but also makes devices able to respond according to the events shown in the images or videos without connecting to the cloud. One of the applications of this intelligent gateway is to use the camera to monitor the place you care about. ... -
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MachineLearningStocks
Using python and scikit-learn to make stock predictions
MachineLearningStocks is a Python-based template project that demonstrates how machine learning can be applied to predicting stock market performance. The project provides a structured workflow that collects financial data, processes features, trains predictive models, and evaluates trading strategies. Using libraries such as pandas and scikit-learn, the repository shows how historical financial indicators can be transformed into machine learning features. -
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...CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities. Pattern Recognition. https://doi.org/10.1016/j.patcog.2021.107977 The preprint version can be obtained at: https://arxiv.org/abs/1810.02897
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TensorLayer
Deep learning and reinforcement learning library for scientists
...TensorLayer has a high-level layer/model abstraction which is effortless to learn. You can learn how deep learning can benefit your AI tasks in minutes through the massive examples. -
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Ad-papers
Papers on Computational Advertising
The Ad-papers repository is a curated collection of influential research papers focused on the fields of advertising technology, recommendation systems, and applied machine learning in online platforms. The repository organizes academic and industry papers that explore how machine learning algorithms can be used to improve ad targeting, user modeling, click-through rate prediction, and personalized recommendation systems. These papers represent key developments in large-scale industrial machine learning systems used by digital advertising platforms. The repository categorizes papers by topic and provides links to research publications, allowing readers to easily explore the evolution of machine learning techniques in advertising and recommendation domains. ... -
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Cloud Annotations
A fast, easy and collaborative open source image annotation tool
...The examples below were created by the Skills Network Team—right here in CV Studio. Create your own project dataset by uploading images and videos. Coming soon, you'll be able to use a pre-compiled dataset so you can hit the ground running. Creating image annotations for your project is easy inside CV Studio. For classification projects, just select and label your images. For object detection, use the integrated tool to highlight target elements in your images. Train your model using the image annotations from the previous step. Practice using cutting-edge tools like Jupyter Notebook, Watson Machine Learning, Elyra, and more. -
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ALAE
Adversarial Latent Autoencoders
...Unlike traditional GANs that directly generate images from random noise, ALAE uses an encoder-decoder architecture that maps images into a structured latent space and then reconstructs them through adversarial training. This design allows the model to learn interpretable latent representations that can be manipulated to control generated image attributes. -
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Libra
Ergonomic machine learning for everyone
An ergonomic machine learning library for non-technical users. Save time. Blaze through ML. -
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TFLearn
Deep learning library featuring a higher-level API for TensorFlow
...Fast prototyping through highly modular built-in neural network layers, regularizers, optimizers, and metrics. Full transparency over Tensorflow. All functions are built over tensors and can be used independently of TFLearn. Powerful helper functions to train any TensorFlow graph, with support of multiple inputs, outputs, and optimizers. Easy and beautiful graph visualization, with details about weights, gradients, activations, and more. Effortless device placement for using multiple CPU/GPU. The high-level API currently supports the most of the recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, etc. -
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Neural Networks Collection
Neural Networks Collection
...So far the project implements: LVQ in several variants, SOM in several variants, Hopfield network and Perceptron. Other neural network types are planned, but not implemented yet. The project can run in two modes: command line tool and Python 7.2 extension. Currently, Python version appears more functional, as it allows easy interaction with algorithms developed by other people. -
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Effective TensorFlow 2
TensorFlow tutorials and best practices
...The repository also demonstrates techniques for improving model performance, optimizing training loops, and debugging TensorFlow programs. Through examples and explanations, the project highlights how developers can structure machine learning code to improve readability and maintainability. The tutorials emphasize both conceptual understanding and implementation details so that users can build more robust deep learning systems. -
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wav2letter++
Facebook AI research's automatic speech recognition toolkit
...It is highly recommended to build KenLM with position-independent code (-fPIC) enabled, to enable python compatibility. After installing, run export KENLM_ROOT_DIR=... so that wav2letter++ can find it. This is needed because KenLM doesn't support a make install step.wav2letter++ expects audio and transcription data to be prepared in a specific format so that they can be read from the pipelines. Each dataset (test/valid/train) needs to be in a separate file with one sample per line. A sample is specified using 4 columns separated by space (or tabs).