Search Results for "transfer function model" - Page 7
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Showing 297 open source projects for "transfer function model"
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1
Emb-GAM
An interpretable and efficient predictor using pre-trained models
...In contrast, generalized additive models (GAMs) can maintain interpretability but often suffer from poor prediction performance due to their inability to effectively capture feature interactions. In this work, we aim to bridge this gap by using pre-trained neural language models to extract embeddings for each input before learning a linear model in the embedding space. The final model (which we call Emb-GAM) is a transparent, linear function of its input features and feature interactions. Leveraging the language model allows Emb-GAM to learn far fewer linear coefficients, model larger interactions, and generalize well to novel inputs. Across a variety of natural-language-processing datasets, Emb-GAM achieves strong prediction performance without sacrificing interpretability. -
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AWS IoT Over-the-air Update Library
Manage the notification of a newly available update
...In addition, the OTA library lets you define application-specific logic for testing, committing, or rolling back a firmware update. The library supports different application protocols like Message Queuing Telemetry Transport (MQTT) and Hypertext Transfer Protocol (HTTP), and provides various configuration options you can fine-tune depending on network type and conditions. This library is distributed under the MIT Open Source License. This library has gone through code quality checks including verification that no function has a GNU Complexity score over 8. -
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PyTorch Transfer-Learning-Library
Transfer Learning Library for Domain Adaptation, Task Adaptation, etc.
TLlib is an open-source and well-documented library for Transfer Learning. It is based on pure PyTorch with high performance and friendly API. Our code is pythonic, and the design is consistent with torchvision. You can easily develop new algorithms or readily apply existing algorithms. We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please... -
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Reinforcement Learning Methods
Simple Reinforcement learning tutorials
Reinforcement-Learning-with-TensorFlow is an educational repository that walks through key reinforcement learning algorithms implemented in TensorFlow. It provides clear code examples for foundational techniques like Q-learning, policy gradients, deep Q-networks, actor-critic methods, and value function approximation within familiar simulation environments. Each algorithm is structured with readable code, explanatory comments, and corresponding environment interaction loops so learners can easily trace how actions, rewards, and model updates connect. The project also includes demo scripts that visualize learning curves and allow students to observe policy improvement over training iterations. ... -
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Tables of the Sun
A Re-implementation of Newcomb's Theory of the Sun
Simon Newcomb published his theory, known as Tables of the Sun, in 1898. It was used by the American Ephemeris and the Astronomical Almanac into the 1980's to compute the daily positions of the Sun. This Theory is a semi-analytic model which expresses quantities as a trigonometric series with numerical coefficients. This type of model produces a compact computer function which has enough accuracy to be useful to most users. My goal is to create a model using C which will reproduce exactly the results published in the Astronomical Almanac for the years 1981, 1982 and 1983. -
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Fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python
...We provide reference implementations of various sequence modeling papers. Recent work by Microsoft and Google has shown that data parallel training can be made significantly more efficient by sharding the model parameters and optimizer state across data parallel workers. These ideas are encapsulated in the new FullyShardedDataParallel (FSDP) wrapper provided by fairscale. Fairseq can be extended through user-supplied plug-ins. Models define the neural network architecture and encapsulate all of the learnable parameters. Criterions compute the loss function given the model outputs and targets. ... -
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STL viewer
STL Viewer for java
... - install unzip distributed stlviewer*.zip - run go into the directory run: java -jar stlviewer.jar In Windows and various OS, you may be able to run it by simply double clicking on the jar file. Navigation - holding / dragging the left mouse button rotates the model - holding / dragging the right mouse button pan the model - rotating the mouse wheel forward moves away from the model / backward moves towards the model - clicking home on the toolbar reverts to the initial view - Pre-requisites Release 0.9.0 Java 11 JDK/JRE. Some JDK/JRE broke the right button pan function. If you encounter the issue, try Tools > "fix mouse interactions" Release 0.8.0 Java 8 JDK/JRE. this is the previous working release. ... -
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Deep learning time series forecasting
Deep learning PyTorch library for time series forecasting
Example image Flow Forecast (FF) is an open-source deep learning for time series forecasting framework. It provides all the latest state-of-the-art models (transformers, attention models, GRUs) and cutting-edge concepts with easy-to-understand interpretability metrics, cloud provider integration, and model serving capabilities. Flow Forecast was the first time series framework to feature support for transformer-based models and remains the only true end-to-end deep learning for time series... -
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CoolplaySpark
Spark Cool Play: Spark source code analysis, Spark class library, etc.
...It serves as a companion resource for the book 深入理解Spark核心思想与源码分析 (In-Depth Understanding of Spark’s Core Concepts and Source Code Analysis). The project contains annotated examples, explanations, and exercises that guide learners through Spark’s architecture, execution model, and source code internals. It is particularly valuable for developers who want to strengthen their understanding of Spark by not only using it as a data processing engine but also exploring how its internals function. Through code analysis and commentary, CoolplaySpark helps readers connect theoretical concepts with practical implementation details. ... -
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Vale
Compiler for the Vale programming language
Vale is a systems programming language and compiler that strives to combine performance, safety, and ease in a way that avoids many of the complexities of traditional memory management techniques. It is ahead-of-time (AOT) compiled, targeting LLVM as a backend, and is statically typed. The language introduces a concept called generational references to provide memory safety without relying on a garbage collector, and aims for “safe without a borrow checker” (i.e. reducing the burden on the... -
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Cisco IOS Device Manager
Cisco IOS Device Management (backup, update image, save conf, reboot)
INFORMATION This program can be used for the following: - To collecting Cisco device information like: hostname, serial, model, IOS image, version, uptime, free memory (to CSV) - To enable the SCP service on the device (automaticly before file transfers) - To make a backup of the running-config and startup-config with SCP (with built-in MD5 hash comparison check) no TFTP/SFTP server needed. - To save the running-config to the startup-config (write memory) - To transfer a new image to the device with SCP (with built-in MD5 hash comparison check) - To change the boot target to the new image (boot system flash:/) It has got the following features: - Error handling - information and error logging to log file (Log will be appended) - information and error logging to CSV file (Log will be overwritten each run) - Transfers retry 3 times - MD5 checks after file transfers to be sure the file is transferred without problems -
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AugLy
A data augmentations library for audio, image, text, and video
AugLy is a data augmentations library that currently supports four modalities (audio, image, text & video) and over 100 augmentations. Each modality’s augmentations are contained within its own sub-library. These sub-libraries include both function-based and class-based transforms, composition operators, and have the option to provide metadata about the transform applied, including its intensity. AugLy is a great library to utilize for augmenting your data in model training, or to evaluate the robustness gaps of your model! We designed AugLy to include many specific data augmentations that users perform in real life on internet platforms like Facebook's -- for example making an image into a meme, overlaying text/emojis on images/videos, reposting a screenshot from social media. ... -
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Symbolic Spice with Maxima
Maxima Script to Calculate Transfer Function of a LTspice Circuit
Script for Maxima that analyzes analog filter circuits. Reads a netlist (e.g. LTspice) and calculates the symbolic transfer function. Supported components: R, L, C, voltage, current, opamp -
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Train Invaders
Jupyter Notebook + Space Invaders
Training a model can be a long long process. In the meantime, have a bit of fun with a competitive space invaders game. See if you can get your name to the top of the leaderboard. -
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eDEX-UI
Customizable science fiction terminal emulator
eDEX-UI is a fullscreen, cross-platform terminal emulator and system monitor designed with a customizable science fiction computer interface. Inspired by the TRON Legacy movie effects, eDEX-UI presents a futuristic look and feel while still being able to function as a full-featured terminal emulator. eDEX-UI offers advanced monitoring support for real-time systems and networks. It also offers full touchscreen support; a directory viewer; and for a completely unique sci-fi experience,... -
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Deep Learning 500 Questions
500 Questions on Deep Learning using a question-and-answer format
...Later chapters explore classic neural network structures such as CNNs, RNNs, and GANs, as well as key applications in computer vision like object detection and image segmentation. The resource also delves into optimization methods, including transfer learning, network architecture design, hyperparameter tuning, model compression, and acceleration techniques. -
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HoloCubic
Multi-function transparent display desktop station
...The hardware design includes versions of the PCB (Naive version, Ironman version) and 3D model files for the enclosure (3D printed or CNC metal). The software leverages LVGL (a GUI library for microcontrollers) and supports surface touch or IMU-based input. Because it connects via WiFi/Bluetooth, it can fetch live data and display dynamic content, bridging embedded display, network, and interactive gadget design. -
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TorchGAN
Research Framework for easy and efficient training of GANs
The torchgan package consists of various generative adversarial networks and utilities that have been found useful in training them. This package provides an easy-to-use API which can be used to train popular GANs as well as develop newer variants. The core idea behind this project is to facilitate easy and rapid generative adversarial model research. TorchGAN is a Pytorch-based framework for designing and developing Generative Adversarial Networks. This framework has been designed to... -
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ReinventCommunity
Jupyter Notebook tutorials for REINVENT 3.2
This repository is a collection of useful jupyter notebooks, code snippets and example JSON files illustrating the use of Reinvent 3.2. -
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Machine Learning & Deep Learning
machine learning and deep learning tutorials, articles
Machine Learning & Deep Learning Tutorials is an open-source repository that provides practical tutorials demonstrating how to implement machine learning and deep learning models using popular frameworks such as TensorFlow and PyTorch. The project focuses on helping learners understand machine learning through hands-on coding examples rather than purely theoretical explanations. Each tutorial walks through the process of building and training models for tasks such as image classification,... -
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Hysteresis
Preisach type hysteresis models implemented in Matlab.
...Szabó, J. Füzi, "Implementation and Identification of Preisach type Hysteresis Models with Everett Function in Closed Form", Journal of Magnetism and Magnetic Materials, vol. 406, 15 pp. 251–258, 2016. [2] Zs. Szabó, “Preisach Functions Leading to Closed Form Permeability“, Physica B, vol. 372, pp. 61-67, 2006. [3] Zs. Szabó, I. Tugyi, Gy. Kádár, J. Füzi, "Identification Procedures for Scalar Preisach Model", Physica B, vol. 343, pp. 142-147, 2004 -
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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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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. ... -
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YApi
A locally deployable, visual interface management platform
YApi makes interface development simpler and more efficient, makes interface management more readable and maintainable, and makes team collaboration more reasonable. Provide basic project grouping, project management, interface management functions. Friendly interface documentation, WebSocket-based multi-person collaboration interface editing function and postman-like testing tools, allowing multi-person collaboration to multiply development efficiency. Based on Mockjs, easy to use and... -
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XLM (Cross-lingual Language Model)
PyTorch original implementation of Cross-lingual Language Model
XLM (Cross-lingual Language Model) is a family of multilingual pretraining methods that align representations across languages to enable strong zero-shot transfer. It popularized objectives like Masked Language Modeling (MLM) across many languages and Translation Language Modeling (TLM) that jointly trains on parallel sentence pairs to tighten cross-lingual alignment.