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gorgonia - Gorgonia is a library that helps facilitate machine learning in Go.
Gorgonia is a library that helps facilitate machine learning in Go. Write and evaluate mathematical equations involving multidimensional arrays easily. If this sounds like Theano or TensorFlow, it's because the idea is quite similar. Specifically, the library is pretty low-level, like Theano, but has higher goals like Tensorflow.The main reason to use Gorgonia is developer comfort. If you're using a Go stack extensively, now you have access to the ability to create production-ready machine learning systems in an environment that you are already familiar and comfortable with.
machine-learning artificial-intelligence neural-network computation-graph differentiation gradient-descent gorgonia deep-learning deeplearning deep-neural-networks automatic-differentiation symbolic-differentiation go-libraryseq2seq - A general-purpose encoder-decoder framework for Tensorflow
A general-purpose encoder-decoder framework for Tensorflow that can be used for Machine Translation, Text Summarization, Conversational Modeling, Image Captioning, and more.The official code used for the Massive Exploration of Neural Machine Translation Architectures paper.
tensorflow translation machine-translation neural-network deeplearningMachine-Learning-Tutorials - machine learning and deep learning tutorials, articles and other resources
This repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources. Other awesome lists can be found in this list. If you want to contribute to this list, please read Contributing Guidelines.
deep-learning-tutorial machine-learning machinelearning deeplearning neural-network neural-networks deep-neural-networks awesome-list awesome list deep-learningnlp-architect - NLP Architect by Intel AI Lab: Python library for exploring the state-of-the-art deep learning topologies and techniques for natural language processing and natural language understanding
NLP Architect is an open-source Python library for exploring state-of-the-art deep learning topologies and techniques for natural language processing and natural language understanding. It is intended to be a platform for future research and collaboration. Framework documentation on NLP models, algorithms, and modules, and instructions on how to contribute can be found at our main documentation site.
deeplearning nlp nlu tensorflow dynet kerasAiLearning - AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP
AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP
fp-growth apriori mahchine-leaning naivebayes svm adaboost kmeans svd pca logistic regression recommendedsystem sklearn scikit-learn nlp deeplearning dnn lstm rnndl4j-examples - Deeplearning4j Examples (DL4J, DL4J Spark, DataVec)
Examples of training different data sets
javafx dl4j deeplearning4j intellij zeppelin-notebook deeplearning artificial-intelligenceAndroidTensorFlowMachineLearningExample - Android TensorFlow MachineLearning Example (Building TensorFlow for Android)
Android TensorFlow MachineLearning Example (Building TensorFlow for Android)
tensorflow tensorflow-tutorials tensorflow-android machine-learning machine-learning-android tensorflow-models tensorflow-examples deep-learning deep-neural-networks deeplearning deep-learning-tutorialludwig - Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code
Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code. All you need to provide is a CSV file containing your data, a list of columns to use as inputs, and a list of columns to use as outputs, Ludwig will do the rest. Simple commands can be used to train models both locally and in a distributed way, and to use them to predict on new data.
deep-learning deeplearning deep-neural-networks deep learning machine-learning machinelearning machine natural-language-processing natural-language-understanding natural-language natural-language-generation computer-vision python3tensorflow-101 - learn code with tensorflow
In folder finetuning, we use tf.slim to finetuning the pretrain model (I use the same method in my porn detection) and use flask to buid a very simple inference system. I deploy a image classification in demo page. It is based on Tensorflow and Flask. Feel free to try.
tensorflow-experiments gan infogan deeplearning flask tensorflowPyTorchZeroToAll - Simple PyTorch Tutorials Zero to ALL!
Quick 3~4 day lecture materials for HKUST students. If you cannot access the GoogleDoc for somehow, please check out pdf files in slides. However, slides in GoogleDrive are always latest. We really appreciate your comments.
deeplearning pytorch tutorialawesome-deep-learning-music - List of articles related to deep learning applied to music
By Yann Bayle (Website, GitHub) from LaBRI (Website, Twitter), Univ. Bordeaux (Website, Twitter), CNRS (Website, Twitter) and SCRIME (Website). The role of this curated list is to gather scientific articles, thesis and reports that use deep learning approaches applied to music. The list is currently under construction but feel free to contribute to the missing fields and to add other resources! To do so, please refer to the How To Contribute section. The resources provided here come from my review of the state-of-the-art for my PhD Thesis for which an article is being written. There are already surveys on deep learning for music generation, speech separation and speaker identification. However, these surveys do not cover music information retrieval tasks that are included in this repository.
awesome awesome-list unicorns list lists resources deeplearning deep-learning deep-neural-networks neural-network neural-networks music music-information-retrieval audio audio-processing article music-genre-classification bib machine-learning researchauto_ml - Automated machine learning for analytics & production
auto_ml is designed for production. Here's an example that includes serializing and loading the trained model, then getting predictions on single dictionaries, roughly the process you'd likely follow to deploy the trained model. All of these projects are ready for production. These projects all have prediction time in the 1 millisecond range for a single prediction, and are able to be serialized to disk and loaded into a new environment after training.
machine-learning data-science automated-machine-learning gradient-boosting scikit-learn machine-learning-pipelines machine-learning-library production-ready automl lightgbm analytics feature-engineering hyperparameter-optimization deep-learning xgboost keras deeplearning tensorflow artificial-intelligencedeeplearning4j-examples - Deeplearning4j Examples (DL4J, DL4J Spark, DataVec)
Examples of training different data sets
javafx dl4j deeplearning4j intellij zeppelin-notebook deeplearning artificial-intelligenceMiDaS - Code for robust monocular depth estimation described in "Ranftl et
MiDaS was trained on 10 datasets (ReDWeb, DIML, Movies, MegaDepth, WSVD, TartanAir, HRWSI, ApolloScape, BlendedMVS, IRS) with multi-objective optimization. The original model that was trained on 5 datasets (MIX 5 in the paper) can be found here. The code was tested with Python 3.7, PyTorch 1.8.0, OpenCV 4.5.1, and timm 0.4.5.
deeplearning single-image-depth-prediction monocular-depth-estimationOpenBot - OpenBot leverages smartphones as brains for low-cost robots
OpenBot leverages smartphones as brains for low-cost robots. We have designed a small electric vehicle that costs about $50 and serves as a robot body. Our software stack for Android smartphones supports advanced robotics workloads such as person following and real-time autonomous navigation. Please read the contribution guidelines. If you are not sure where to start have a look at the open issues.
android education arduino research robot robotics smartphone deeplearning openbotInvoiceNet - Deep neural network to extract intelligent information from invoice documents.
Deep neural network to extract intelligent information from invoice documents. The InvoiceNet logo was designed by Sidhant Tibrewal. Check out his work for some more beautiful designs.
information-retrieval deep-neural-networks deep-learning invoices keras information-extraction classification invoice billing deeplearning keras-neural-networks invoice-pdf invoice-management keras-tensorflow invoice-software invoice-insight invoice-parserBMW-TensorFlow-Training-GUI - This repository allows you to get started with a gui based training a State-of-the-art Deep Learning model with little to no configuration needed! NoCode training with TensorFlow has never been so easy
This repository allows you to get started with training a State-of-the-art Deep Learning model with little to no configuration needed! You provide your labeled dataset and you can start the training right away and monitor it with TensorBoard. You can even test your model with our built-in Inference REST API. Training with TensorFlow has never been so easy.
docker gui deep-neural-networks computer-vision deep-learning neural-network tensorflow rest-api tensorboard resnet deeplearning object-detection nvidia-docker computervision objectdetection no-code tensorflow-training detection-api tensorflow-gui inference-apispago - Self-contained Machine Learning and Natural Language Processing library in Go
A Machine Learning library written in pure Go designed to support relevant neural architectures in Natural Language Processing. spaGO is self-contained, in that it uses its own lightweight computational graph framework for both training and inference, easy to understand from start to finish.
nlp machine-learning natural-language-processing deep-learning neural-network automatic-differentiation artificial-intelligence recurrent-networks lstm computation-graph question-answering bart automatic-translation deeplearning language-model bert transformer-architecture bert-as-service named-entities-recognition
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