Search Results for "ai" - Page 54
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Showing 1429 open source projects for "ai"
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Snips NLU
Snips Python library to extract meaning from text
...The exact output is a bit richer, the point here is to give a glimpse on what kind of information can be extracted. Behind every chatbot and voice assistant lies a common piece of technology: Natural Language Understanding (NLU). Anytime a user interacts with an AI using natural language, their words need to be translated into a machine-readable description of what they meant. The NLU engine first detects what the intention of the user is (a.k.a. intent), then extracts the parameters (called slots) of the query. The developer can then use this to determine the appropriate action or response. -
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Dragonfire
The open-source virtual assistant for Ubuntu based Linux distributions
Dragonfire is the open-source virtual assistant project for Ubuntu-based Linux distributions. Her main objective is to serve as a command and control interface to the helmet user. So that you will be able to give orders just by using your voice commands and your eye movements. That makes the helmet handsfree. We are planning to ship Dragonfire as a preinstalled software package on DragonOS Linux Distribution. DragonOS will be a Linux distribution specially designed for the helmet. It will... -
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SMAC
SMAC: The StarCraft Multi-Agent Challenge
SMAC (StarCraft II Multi-Agent Challenge) is a benchmark environment for cooperative multi-agent reinforcement learning (MARL), based on real-time strategy (RTS) game scenarios in StarCraft II. It allows researchers to test algorithms where multiple units (agents) must collaborate to win battles against built-in game AI opponents. SMAC provides a controlled testbed for studying decentralized execution and centralized training paradigms in MARL. -
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gpt2-client
Easy-to-use TensorFlow Wrapper for GPT-2 117M, 345M, 774M, etc.
GPT-2 is a Natural Language Processing model developed by OpenAI for text generation. It is the successor to the GPT (Generative Pre-trained Transformer) model trained on 40GB of text from the internet. It features a Transformer model that was brought to light by the Attention Is All You Need paper in 2017. The model has 4 versions - 124M, 345M, 774M, and 1558M - that differ in terms of the amount of training data fed to it and the number of parameters they contain. Finally, gpt2-client is a... -
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GPT-2 FR
GPT-2 French demo | Démo française de GPT-2
OpenAI GPT-2 model trained on four different datasets in French. Books in French, French film scripts, reports of parliamentary debates, Tweet by Emmanuel Macron, allowing to generate text. Tensorflow and gpt-2-simple are required in order to fine-tune GPT-2. Create an environment then install the two packages pip install tensorflow==1.14 gpt-2-simple. A script and a notebook are available in the src folder to fine-tune GPT-2 on your own datasets. The output of each workout, i.e. the folder... -
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DeepSDF
Learning Continuous Signed Distance Functions for Shape Representation
DeepSDF is a deep learning framework for continuous 3D shape representation using Signed Distance Functions (SDFs), as presented in the CVPR 2019 paper DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation by Park et al. The framework learns a continuous implicit function that maps 3D coordinates to their corresponding signed distances from object surfaces, allowing compact, high-fidelity shape modeling. Unlike traditional discrete voxel grids or meshes, DeepSDF... -
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Jarvis
Personal Assistant for Linux and macOS
Jarvis is a simple personal assistant for Linux, MacOS and Windows which works on the command line. He can talk to you if you enable his voice. He can tell you the weather, he can find restaurants and other places near you. He can do some great stuff for you. In order to start Jarvis just clone this repository and run python installer. Run Jarvis from anywhere by command jarvis. You can start by typing help within the Jarvis command line to check what Jarvis can do for you. Plugins may be... -
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PyTorch-BigGraph
Generate embeddings from large-scale graph-structured data
PyTorch-BigGraph (PBG) is a system for learning embeddings on massive graphs—think billions of nodes and edges—using partitioning and distributed training to keep memory and compute tractable. It shards entities into partitions and buckets edges so that each training pass only touches a small slice of parameters, which drastically reduces peak RAM and enables horizontal scaling across machines. PBG supports multi-relation graphs (knowledge graphs) with relation-specific scoring functions,... -
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NiftyNet
An open-source convolutional neural networks platform for research
An open-source convolutional neural networks platform for medical image analysis and image-guided therapy. NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNNs) platform for research in medical image analysis and image-guided therapy. NiftyNet’s modular structure is designed for sharing networks and pre-trained models. Using this modular structure you can get started with established pre-trained networks using built-in tools. Adapt existing networks to your imaging... -
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DCVGAN
DCVGAN: Depth Conditional Video Generation, ICIP 2019.
This paper proposes a new GAN architecture for video generation with depth videos and color videos. The proposed model explicitly uses the information of depth in a video sequence as additional information for a GAN-based video generation scheme to make the model understands scene dynamics more accurately. The model uses pairs of color video and depth video for training and generates a video using the two steps. Generate the depth video to model the scene dynamics based on the geometrical... -
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MMF
A modular framework for vision & language multimodal research
MMF is a modular framework for vision and language multimodal research from Facebook AI Research. MMF contains reference implementations of state-of-the-art vision and language models and has powered multiple research projects at Facebook AI Research. MMF is designed from ground up to let you focus on what matters, your model, by providing boilerplate code for distributed training, common datasets and state-of-the-art pre-trained baselines out-of-the-box. -
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DCGAN in TensorLayerX
The Simplest DCGAN Implementation
This is an implementation of Deep Convolutional Generative Adversarial Networks. First, download the aligned face images from google or baidu to a data folder. Please place dataset 'img_align_celeba.zip' under 'data/celebA/' by default. -
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AIAlpha
Use unsupervised and supervised learning to predict stocks
AIAlpha is a machine learning project focused on building predictive models for financial markets and algorithmic trading strategies. The repository explores how artificial intelligence techniques can analyze historical financial data and generate predictions about asset price movements. It provides a research-oriented environment where users can experiment with data processing pipelines, model training workflows, and quantitative trading strategies. The project typically involves collecting... -
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The Google Cloud Developer's Cheat Sheet
Cheat sheet for Google Cloud developers
Every product in the Google Cloud family described in <=4 words (with liberal use of hyphens and slashes) by the Google Developer Relations Team. This list only includes products that are publicly available. There are several products in pre-release/private-alpha that will not be included until they go public beta or GA. Many of these products have a free tier. There is also a free trial that will enable you try almost everything. API platforms and ecosystems, developer and management tools,... -
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Project Malmo
A platform for Artificial Intelligence experimentation on Minecraft
...Project Malmo sets out to address these core research challenges, addressing them by integrating (deep) reinforcement learning, cognitive science, and many ideas from artificial intelligence. The Malmo platform is a sophisticated AI experimentation platform built on top of Minecraft, and designed to support fundamental research in artificial intelligence. The Project Malmo platform consists of a mod for the Java version, and code that helps artificial intelligence agents sense and act within the Minecraft environment. The two components can run on Windows, Linux, or Mac OS, and researchers can program their agents in any programming language they’re comfortable with. -
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GPT2-Pytorch with Text-Generator
Simple Text-Generator with OpenAI gpt-2 Pytorch Implementation
GPT-2 PyTorch is a compact educational implementation of OpenAI’s GPT-2 text generator in PyTorch. It uses pretrained weights converted for PyTorch rather than training the language model from scratch. Users can begin generation from a supplied prompt or request unconditional samples. Command-line options control sample count, batch size, output length, temperature, and top-k filtering. The repository includes a runnable Python script, dependency file, and Google Colab notebook for faster... -
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FID score for PyTorch
Compute FID scores with PyTorch
This is a port of the official implementation of Fréchet Inception Distance to PyTorch. FID is a measure of similarity between two datasets of images. It was shown to correlate well with human judgement of visual quality and is most often used to evaluate the quality of samples of Generative Adversarial Networks. FID is calculated by computing the Fréchet distance between two Gaussians fitted to feature representations of the Inception network. The weights and the model are exactly the same... -
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MAML-Pytorch
Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning
MAML-Pytorch is a PyTorch implementation of Model-Agnostic Meta-Learning for supervised learning experiments. It focuses on reproducing and exploring the MAML approach for few-shot learning research. The repository supports MiniImagenet and Omniglot, two common benchmark datasets for meta-learning experiments. It includes separate training scripts, dataset loaders, learner components, and meta-learning logic. The project also notes that MAML can be difficult to train and presents the... -
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TGAN
Generative adversarial training for generating synthetic tabular data
We are happy to announce that our new model for synthetic data called CTGAN is open-sourced. The new model is simpler and gives better performance on many datasets. TGAN is a tabular data synthesizer. It can generate fully synthetic data from real data. Currently, TGAN can generate numerical columns and categorical columns. TGAN has been developed and runs on Python 3.5, 3.6 and 3.7. Also, although it is not strictly required, the usage of a virtualenv is highly recommended in order to avoid... -
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MUSE
A library for Multilingual Unsupervised or Supervised word Embeddings
MUSE is a framework for learning multilingual word embeddings that live in a shared space, enabling bilingual lexicon induction, cross-lingual retrieval, and zero-shot transfer. It supports both supervised alignment with seed dictionaries and unsupervised alignment that starts without parallel data by using adversarial initialization followed by Procrustes refinement. The code can align pre-trained monolingual embeddings (such as fastText) across dozens of languages and provides standardized... -
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PyTorch pretrained BigGAN
PyTorch implementation of BigGAN with pretrained weights
An op-for-op PyTorch reimplementation of DeepMind's BigGAN model with the pre-trained weights from DeepMind. This repository contains an op-for-op PyTorch reimplementation of DeepMind's BigGAN that was released with the paper Large Scale GAN Training for High Fidelity Natural Image Synthesis. This PyTorch implementation of BigGAN is provided with the pretrained 128x128, 256x256 and 512x512 models by DeepMind. We also provide the scripts used to download and convert these models from the... -
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Improved GAN
Code for the paper "Improved Techniques for Training GANs"
Improved-GAN is the official code release from OpenAI accompanying the research paper Improved Techniques for Training GANs. It provides implementations of experiments conducted on datasets such as MNIST, SVHN, CIFAR-10, and ImageNet. The project focuses on demonstrating enhanced training methods for Generative Adversarial Networks, addressing stability and performance issues that were common in earlier GAN models. The repository includes training scripts, evaluation methods, and pretrained... -
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Finetune Transformer LM
Code for "Improving Language Understanding by Generative Pre-Training"
finetune-transformer-lm is a research codebase that accompanies the paper “Improving Language Understanding by Generative Pre-Training,” providing a minimal implementation focused on fine-tuning a transformer language model for evaluation tasks. The repository centers on reproducing the ROCStories Cloze Test result and includes a single-command training workflow to run the experiment end to end. It documents that runs are non-deterministic due to certain GPU operations and reports a median... -
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InfoGAN
Code for reproducing key results in the paper
The InfoGAN repository contains the original implementation used to reproduce the results in the paper “InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets”. InfoGAN is a variant of the GAN (Generative Adversarial Network) architecture that aims to learn disentangled and interpretable latent representations by maximizing the mutual information between a subset of the latent codes and the generated outputs. That extra incentive encourages the... -
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Market Reporter
Automatic Generation of Brief Summaries of Time-Series Data
Market Reporter automatically generates short comments that describe time series data of stock prices, FX rates, etc. This is an implementation of Murakami et al. This tool stores data to Amazon S3. Ask the manager to give you AmazonS3FullAccess and issue a credential file. For details, please read AWS Identity and Access Management. Install Docker and Docker Compose. Edit envs/docker-compose.yaml according to your environment. Then, launch containers by docker-compose. We recommend to use...
