Search Results for "simple-quiz" - Page 4
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Showing 104 open source projects for "simple-quiz"
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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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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 achieves 5--9x speed-up over cuDNN-optimized LSTM on classification and question answering datasets, and delivers stronger results than LSTM and convolutional models. ... -
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PyTorch SimCLR
PyTorch implementation of SimCLR: A Simple Framework
For quite some time now, we know about the benefits of transfer learning in Computer Vision (CV) applications. Nowadays, pre-trained Deep Convolution Neural Networks (DCNNs) are the first go-to pre-solutions to learn a new task. These large models are trained on huge supervised corpora, like the ImageNet. And most important, their features are known to adapt well to new problems. This is particularly interesting when annotated training data is scarce. In situations like this, we take the... -
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BudgetML
Deploy a ML inference service on a budget in 10 lines of code
...BudgetML is perfect for practitioners who would like to quickly deploy their models to an endpoint, but not waste a lot of time, money, and effort trying to figure out how to do this end-to-end. We built BudgetML because it's hard to find a simple way to get a model in production fast and cheaply. Deploying from scratch involves learning too many different concepts like SSL certificate generation, Docker, REST, Uvicorn/Gunicorn, backend servers etc., that are simply not within the scope of a typical data scientist. BudgetML is our answer to this challenge. It is supposed to be fast, easy, and developer-friendly. ... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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TensorLayer
Deep learning and reinforcement learning library for scientists
TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides an extensive collection of customizable neural layers to build advanced AI models quickly, based on this, the community open-sourced mass tutorials and applications. TensorLayer is awarded the 2017 Best Open Source Software by the ACM Multimedia Society. This project can also be found at OpenI and Gitee. 3.0.0 has been pre-released, the current version... -
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gradslam
gradslam is an open source differentiable dense SLAM library
gradslam is an open-source framework providing differentiable building blocks for simultaneous localization and mapping (SLAM) systems. We enable the usage of dense SLAM subsystems from the comfort of PyTorch. The question of “representation” is central in the context of dense simultaneous localization and mapping (SLAM). Newer learning-based approaches have the potential to leverage data or task performance to directly inform the choice of representation. However, learning representations... -
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EfficientNet Keras
Implementation of EfficientNet model. Keras and TensorFlow Keras
...In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet. -
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Forecasting Best Practices
Time Series Forecasting Best Practices & Examples
Time series forecasting is one of the most important topics in data science. Almost every business needs to predict the future in order to make better decisions and allocate resources more effectively. This repository provides examples and best practice guidelines for building forecasting solutions. The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in forecasting algorithms to build solutions and operationalize them. Rather than... -
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Magnitude
A fast, efficient universal vector embedding utility package
A feature-packed Python package and vector storage file format for utilizing vector embeddings in machine learning models in a fast, efficient, and simple manner developed by Plasticity. It is primarily intended to be a simpler / faster alternative to Gensim but can be used as a generic key-vector store for domains outside NLP. It offers unique features like out-of-vocabulary lookups and streaming of large models over HTTP. Published in our paper at EMNLP 2018 and available on arXiv. -
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TensorNets
High level network definitions with pre-trained weights in TensorFlow
...For example, all the inception variants are implemented as about 500 lines of code in TensorNets while 2000+ lines in official TensorFlow models. Reproducibility. You can always reproduce the original results with simple APIs including feature extractions. -
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Knock Knock
Get notified when your training ends
...Training deep learning models often takes hours or even days, making it inconvenient for engineers to constantly monitor progress manually. The library solves this problem by adding simple decorators or command-line commands that automatically send notifications when a process completes or crashes. These alerts can be delivered through several communication platforms such as email, Slack, Telegram, or other messaging services. The goal of the project is to allow developers to monitor experiments remotely without needing to stay connected to the training environment. ... -
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imgaug
Image augmentation for machine learning experiments
imgaug is a library for image augmentation in machine learning experiments. It supports a wide range of augmentation techniques, allows to easily combine these and to execute them in random order or on multiple CPU cores, has a simple yet powerful stochastic interface and can not only augment images but also key points/landmarks, bounding boxes, heatmaps and segmentation maps. Affine transformations, perspective transformations, contrast changes, gaussian noise, dropout of regions, hue/saturation changes, cropping/padding, blurring, etc. Rotate image and segmentation map on it by the same value sampled. ... -
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Image Quality Assessment
Convolutional Neural Networks to predict aesthetic quality of images
...The goal of the project is to automatically evaluate images based on perceived quality factors such as composition, clarity, and visual appeal. Instead of relying on simple image statistics, the system learns patterns that correlate with human judgments about image aesthetics and technical quality. The repository includes code for training models, performing inference, and evaluating predicted scores against labeled datasets. It also provides utilities for image preprocessing and data management that help prepare datasets for training deep learning models. -
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Dive-into-DL-TensorFlow2.0
Dive into Deep Learning
This project changes the MXNet code implementation in the original book "Learning Deep Learning by Hand" to TensorFlow2 implementation. After consulting Mr. Li Mu by the tutor of archersama , the implementation of this project has been agreed by Mr. Li Mu. Original authors: Aston Zhang, Li Mu, Zachary C. Lipton, Alexander J. Smola and other community contributors. There are some differences between the Chinese and English versions of this book . This project mainly focuses on TensorFlow2... -
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Machine Learning From Scratch
Bare bones NumPy implementations of machine learning models
...The goal of the project is to help learners understand how machine learning algorithms work internally by building them step by step from fundamental mathematical operations. The repository includes implementations of algorithms ranging from simple models such as linear regression and logistic regression to more complex techniques such as decision trees, support vector machines, clustering methods, and neural networks. Because the code avoids external machine learning libraries, it exposes the full logic behind model training, optimization, and prediction processes. The project also provides examples and explanations that illustrate how the algorithms behave and how different components interact during training. -
16
Coach
Enables easy experimentation with state of the art algorithms
...The available environments allow testing the agent in different fields such as robotics, autonomous driving, games and more. It exposes a set of easy-to-use APIs for experimenting with new RL algorithms and allows simple integration of new environments to solve. Coach collects statistics from the training process and supports advanced visualization techniques for debugging the agent being trained. Coach supports many state-of-the-art reinforcement learning algorithms, which are separated into three main classes - value optimization, policy optimization, and imitation learning. ... -
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RoboSat
Semantic segmentation on aerial and satellite imagery
RoboSat is an end-to-end pipeline written in Python 3 for feature extraction from aerial and satellite imagery. Features can be anything visually distinguishable in the imagery for example: buildings, parking lots, roads, or cars. -
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Easy-TensorFlow
Simple and comprehensive tutorials in TensorFlow
...There is a necessity to address the motivations for this project. TensorFlow is one of the deep learning frameworks available with the largest community. This repository is dedicated to suggesting a simple path to learn TensorFlow. In addition to the aforementioned points, the large community of TensorFlow enriches the developers with the answer to almost all the questions one may encounter. Furthermore, since most of the developers are using TensorFlow for code development, having hands-on on TensorFlow is a necessity these days. ... -
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Deep Reinforcement Learning for Keras
Deep Reinforcement Learning for Keras.
...You can use built-in Keras callbacks and metrics or define your own. Even more so, it is easy to implement your own environments and even algorithms by simply extending some simple abstract classes. Documentation is available online. -
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Face Recognition
World's simplest facial recognition api for Python & the command line
Face Recognition is the world's simplest face recognition library. It allows you to recognize and manipulate faces from Python or from the command line using dlib's (a C++ toolkit containing machine learning algorithms and tools) state-of-the-art face recognition built with deep learning. Face Recognition is highly accurate and is able to do a number of things. It can find faces in pictures, manipulate facial features in pictures, identify faces in pictures, and do face recognition on a... -
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Deep Learning with Keras and Tensorflow
Introduction to Deep Neural Networks with Keras and Tensorflow
...The goal of libgpuarray is (from the documentation) make a common GPU ndarray (n dimensions array) that can be reused by all projects that is as future proof as possible, while keeping it easy to use for simple need/quick test. The easiest way to get (most) these is to use an all-in-one installer such as Anaconda from Continuum. These are available for multiple architectures. -
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Neural Photo Editor
A simple interface for editing natural photos
Neural Photo Editor is an experimental machine learning application that demonstrates how generative neural networks can be used as an interactive photo editing tool. The project implements the system described in the research paper Neural Photo Editing with Introspective Adversarial Networks, which introduces a generative model capable of modifying images in semantically meaningful ways. Instead of editing images by directly manipulating pixels, the software allows users to influence... -
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AWS IoT Arduino Yún SDK
SDK for connecting to AWS IoT from an Arduino Yún
The AWS-IoT-Arduino-Yún-SDK allows developers to connect their Arduino Yún compatible Board to AWS IoT. By connecting the device to the AWS IoT, users can securely work with the message broker, rules and the Thing Shadow provided by AWS IoT and with other AWS services like AWS Lambda, Amazon Kinesis, Amazon S3, etc. The AWS-IoT-Arduino-Yún-SDK consists of two parts, which take use of the resources of the two chips on Arduino Yún, one for native Arduino IDE API access and the other for... -
24
HYBRYD
Library written in C with Python API for IPv6 networking
This project is a rewritten of an initial project that I've called GLUE and created in 2005. I'm trying to readapt it for Python 2.7.3 and GCC 4.6.3 The library has to be build as a simple Python extension using >python setup.py install and allows to create different kind of servers, clients or hybryds (clients-servers) over (TCP/UDP) using the Ipv6 Protocol. The architecture of the code is based on brain architecture. Will put an IPv6 adress active available as soon as possible so that you can download pieces of codes. ... -
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ProximityForest
Efficient Approximate Nearest Neighbors for General Metric Spaces
A proximity forest is a data structure that allows for efficient computation of approximate nearest neighbors of arbitrary data elements in a metric space. See: O'Hara and Draper, "Are You Using the Right Approximate Nearest Neighbor Algorithm?", WACV 2013 (best student paper award). One application of a ProximityForest is given in the following CVPR publication: Stephen O'Hara and Bruce A. Draper, "Scalable Action Recognition with a Subspace Forest," IEEE Conference on Computer...
