Search Results for "tensorflow" - Page 6
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Showing 244 open source projects for "tensorflow"
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Trax
Deep learning with clear code and speed
...). It is also actively used for research and includes new models like the Reformer and new RL algorithms like AWR. Trax has bindings to a large number of deep learning datasets, including Tensor2Tensor and TensorFlow datasets. You can use Trax either as a library from your own python scripts and notebooks or as a binary from the shell, which can be more convenient for training large models. It runs without any changes on CPUs, GPUs and TPUs. -
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GPT Neo
An implementation of model parallel GPT-2 and GPT-3-style models
An implementation of model & data parallel GPT3-like models using the mesh-tensorflow library. If you're just here to play with our pre-trained models, we strongly recommend you try out the HuggingFace Transformer integration. Training and inference is officially supported on TPU and should work on GPU as well. This repository will be (mostly) archived as we move focus to our GPU-specific repo, GPT-NeoX. NB, while neo can technically run a training step at 200B+ parameters, it is very... -
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gpt-2-simple
Python package to easily retrain OpenAI's GPT-2 text-generating model
...). If you are training in the cloud, using a Colaboratory notebook or a Google Compute Engine VM w/ the TensorFlow Deep Learning image is strongly recommended. (as the GPT-2 model is hosted on GCP) You can use gpt-2-simple to retrain a model using a GPU for free in this Colaboratory notebook, which also demos additional features of the package. Note: Development on gpt-2-simple has mostly been superceded by aitextgen, which has similar AI text generation capabilities with more efficient training time. -
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Spleeter
Deezer source separation library including pretrained models
Spleeter is the Deezer source separation library with pretrained models written in Python and using Tensorflow. It makes it easy to train music source separation models (assuming you have a dataset of isolated sources), and provides already trained state of the art models for performing various flavours of separation. 2 stems and 4 stems models have state of the art performances on the musdb dataset. Spleeter is also very fast as it can perform separation of audio files to 4 stems 100x faster... -
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Robust Video Matting (RVM)
Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX
We introduce a robust, real-time, high-resolution human video matting method that achieves new state-of-the-art performance. Our method is much lighter than previous approaches and can process 4K at 76 FPS and HD at 104 FPS on an Nvidia GTX 1080Ti GPU. Unlike most existing methods that perform video matting frame-by-frame as independent images, our method uses a recurrent architecture to exploit temporal information in videos and achieves significant improvements in temporal coherence and... -
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CleverHans
An adversarial example library for constructing attacks
This repository contains the source code for CleverHans, a Python library to benchmark machine learning systems' vulnerability to adversarial examples. You can learn more about such vulnerabilities on the accompanying blog. The CleverHans library is under continual development, always welcoming contributions of the latest attacks and defenses. In particular, we always welcome help with resolving the issues currently open. Since v4.0.0, CleverHans supports 3 frameworks: JAX, PyTorch, and TF2.... -
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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... -
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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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Machine Learning Collection
A resource for learning about Machine learning & Deep Learning
A resource for learning about Machine learning & Deep Learning. In this repository, you will find tutorials and projects related to Machine Learning. I try to make the code as clear as possible, and the goal is be to used as a learning resource and a way to look up problems to solve specific problems. For most, I have also done video explanations on YouTube if you want a walkthrough for the code. -
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DeepSpeech
Open source embedded speech-to-text engine
DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers. DeepSpeech is an open-source Speech-To-Text engine, using a model trained by machine learning techniques based on Baidu's Deep Speech research paper. Project DeepSpeech uses Google's TensorFlow to make the implementation easier. A pre-trained English model is available for use and can be downloaded following... -
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Hands-on Unsupervised Learning
Code for Hands-on Unsupervised Learning Using Python (O'Reilly Media)
... be applied; this is where unsupervised learning comes in. Unsupervised learning can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel provides practical knowledge on how to apply unsupervised learning using two simple, production-ready Python frameworks - scikit-learn and TensorFlow. With the hands-on examples and code provided, you will identify difficult-to-find patterns in data. -
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Text Gen
Almost state of art text generation library
Almost state of art text generation library. Text gen is a python library that allow you build a custom text generation model with ease. Something sweet built with Tensorflow and Pytorch(coming soon). Load your data, your data must be in a text format. Download the example data from the example folder. Tune your model to know the best optimizer, activation method to use. -
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Objectron
A dataset of short, object-centric video clips
The Objectron dataset is a collection of short, object-centric video clips, which are accompanied by AR session metadata that includes camera poses, sparse point-clouds and characterization of the planar surfaces in the surrounding environment. In each video, the camera moves around the object, capturing it from different angles. The data also contain manually annotated 3D bounding boxes for each object, which describe the object’s position, orientation, and dimensions. The dataset consists... -
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TTS
Deep learning for text to speech
TTS is a library for advanced Text-to-Speech generation. It's built on the latest research, was designed to achieve the best trade-off among ease-of-training, speed, and quality. TTS comes with pre-trained models, tools for measuring dataset quality, and is already used in 20+ languages for products and research projects. Released models in PyTorch, Tensorflow and TFLite. Tools to curate Text2Speech datasets underdataset_analysis. Demo server for model testing. Notebooks for extensive model... -
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U-Net Fusion RFI
U-Net for RFI Detection based on @jakeret's implementation
See original code here: https://github.com/jakeret/tf_unet Currently this project is based on Tensorflow 1.13 code base and there are no plans to transfer to TF version 2. The primary improvements to this code base include a training and evaluation framework, along with a fusion based approach to detection, combining a number of models (currently hard coded to two trained models) along with Sum Threshold as an additional "expert." Additional work is being done to add custom layers... -
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Keras TCN
Keras Temporal Convolutional Network
TCNs exhibit longer memory than recurrent architectures with the same capacity. Performs better than LSTM/GRU on a vast range of tasks (Seq. MNIST, Adding Problem, Copy Memory, Word-level PTB...). Parallelism (convolutional layers), flexible receptive field size (possible to specify how far the model can see), stable gradients (backpropagation through time, vanishing gradients). The usual way is to import the TCN layer and use it inside a Keras model. The receptive field is defined as the... -
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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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EmguTF
Emgu TF is a cross platform .Net wrapper for the Tensorflow library
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Business Bot Platform
Chatbot platform for various instant messenger
... various instant messenger APIs. It is flexible through its plugin architecture and has a repository for chatbot applications (e.g. Wikipedia, Wiktionary, Calculator) which are installable by one-click. Also supporting RiveScript and AIML 2.0. BBP is a web application which handles all incoming and outgoing instant messages and comes with a dashboard for all platform activities. Moreover, it supports artificial intelligence (e.g. Ms LUIS and TensorFlow) BBP is based on Apache, MySQL, and Tomcat -
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TFLearn
Deep learning library featuring a higher-level API for TensorFlow
TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed up experimentations while remaining fully transparent and compatible with it. Easy-to-use and understand high-level API for implementing deep neural networks, with tutorials and examples. Fast prototyping through highly modular built-in neural network layers, regularizers, optimizers, and metrics. Full transparency... -
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TurboTransformers
Fast and user-friendly runtime for transformer inference
TurboTransformers is a high-performance inference framework optimized for running Transformer models efficiently on CPUs and GPUs. It improves latency and throughput for NLP applications. -
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TensorFlow Object Counting API
The TensorFlow Object Counting API is an open source framework
The TensorFlow Object Counting API is an open source framework built on top of TensorFlow and Keras that makes it easy to develop object counting systems. Please contact if you need professional object detection & tracking & counting project with super high accuracy and reliability! You can train TensorFlow models with your own training data to built your own custom object counter system! If you want to learn how to do it, please check one of the sample projects, which cover some of the theory... -
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Lambda Networks
Implementation of LambdaNetworks, a new approach to image recognition
Implementation of λ Networks, a new approach to image recognition that reaches SOTA on ImageNet. The new method utilizes λ layer, which captures interactions by transforming contexts into linear functions, termed lambdas, and applying these linear functions to each input separately. Shinel94 has added a Keras implementation! It won't be officially supported in this repository, so either copy / paste the code under ./lambda_networks/tfkeras.py or make sure to install tensorflow and keras before... -
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TensorFlow Course
Simple and ready-to-use tutorials for TensorFlow
This repository houses a highly popular (~16k stars) set of TensorFlow tutorials and example code aimed at beginners and intermediate users. It includes Jupyter notebooks and scripts that cover neural network fundamentals, model training, deployment, and more, with support for Google Colab.
