Search Results for "train" - Page 6
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Showing 145 open source projects for "train"
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Tensorpack
A Neural Net Training Interface on TensorFlow, with focus on speed
Tensorpack is a neural network training interface based on TensorFlow v1. Uses TensorFlow in the efficient way with no extra overhead. On common CNNs, it runs training 1.2~5x faster than the equivalent Keras code. Your training can probably gets faster if written with Tensorpack. Scalable data-parallel multi-GPU / distributed training strategy is off-the-shelf to use. Squeeze the best data loading performance of Python with tensorpack.dataflow. Symbolic programming (e.g. tf.data) does not... -
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LUMINOTH
Deep Learning toolkit for Computer Vision
...It was created to simplify the process of building and experimenting with deep learning models capable of identifying objects within images. Luminoth includes support for popular object detection architectures such as Faster R-CNN and SSD, enabling developers to train models on datasets like COCO and Pascal VOC. The toolkit provides command-line utilities for dataset management, training, and inference, making it easier to integrate into research workflows and production systems. Although the project is no longer actively maintained, it remains a useful educational and experimental platform for studying object detection pipelines and deep learning workflows. -
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3D ResNets for Action Recognition
3D ResNets for Action Recognition (CVPR 2018)
We uploaded the pretrained models described in this paper including ResNet-50 pretrained on the combined dataset with Kinetics-700 and Moments in Time. We significantly updated our scripts. If you want to use older versions to reproduce our CVPR2018 paper, you should use the scripts in the CVPR2018 branch. -
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DIGITS
Deep Learning GPU training system
The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning into the hands of engineers and data scientists. DIGITS can be used to rapidly train the highly accurate deep neural network (DNNs) for image classification, segmentation and object detection tasks. DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real-time with advanced visualizations, and selecting the best performing model from the results browser for deployment. ... -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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Siamese and triplet learning
Siamese and triplet networks with online triplet mining in PyTorch
...These types of networks learn to map images into a compact feature space where the distance between vectors reflects the similarity between inputs. Such embeddings are commonly used in applications like face recognition, image similarity search, and few-shot learning. The repository demonstrates how to train these models using contrastive loss and triplet loss functions, which encourage embeddings of similar samples to be close while pushing dissimilar samples farther apart. It includes data loaders, training scripts, neural network architectures, and evaluation metrics that allow researchers to experiment with different embedding learning strategies. ... -
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kcws
Deep Learning Chinese Word Segment
Deep learning chinese word segment. Install the bazel code construction tool and install tensorflow (currently this project requires tf 1.0.0alpha version or above) Switch to the code directory of this project and run ./configure. Compile background service. Pay attention to the public account of waiting for words and reply to kcws to get the corpus download address. Extract the corpus to a directory. Change to the code directory.After installing tensorflow, switch to the kcws code... -
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DMTK
Microsoft Distributed Machine Learning Toolkit
The Microsoft Distributed Machine Learning Toolkit (DMTK) is an open-source framework created to support scalable machine learning across distributed computing environments. Developed by Microsoft Research, the toolkit provides infrastructure and algorithms designed to train large models efficiently on clusters of machines rather than a single system. At its core is a parameter-server architecture called Multiverso, which manages model parameters and synchronizes updates across distributed training processes. This architecture allows developers to build machine learning systems capable of processing massive datasets and training complex models with reduced infrastructure requirements. ... -
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DeepLearningProject
An in-depth machine learning tutorial
This tutorial tries to do what most Most Machine Learning tutorials available online do not. It is not a 30 minute tutorial that teaches you how to "Train your own neural network" or "Learn deep learning in under 30 minutes". It's a full pipeline which you would need to do if you actually work with machine learning - introducing you to all the parts, and all the implementation decisions and details that need to be made. The dataset is not one of the standard sets like MNIST or CIFAR, you will make you very own dataset. ... -
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Training Image Operators from Samples
Tools to train Image Operators automatically from a set of samples.
TRIOS - Training Image Operators from Samples is a set of tools to bring Image Processing closer to scientists in general. It is capable of estimating an operator between two images using only pairs of samples that contain an input image and the desired output. The operator is saved to a file and can be applied to any image. -
Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI. Switch between models without switching platforms.
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H2O-3
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning
H2O-3 is an open-source machine learning platform designed to build scalable and distributed machine learning models across large datasets. The system operates as an in-memory computing platform that allows data scientists to train models quickly using distributed resources. It supports many machine learning algorithms including generalized linear models, gradient boosting machines, deep learning networks, and ensemble techniques. The platform provides interfaces for multiple programming languages such as Python, R, Java, and Scala, making it accessible to a wide range of developers and data scientists. ... -
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Intelligent Keyword Miner
Intelligent SEO keyword miner and predicing tool
...This program is intelligent as it predicts what you may want to search next, based on your personal trends. After searching the suggestions, you can choose to reset or train it further. Programs that have similar idea are: Google AdWords, SERPWoo's Keyword Finder, Wordpot, and others. Difference is, this program is intelligent and it accepts your input data and then predicts keywords based on your likes or dislikes. As the main engine, it uses the SMOReg algorithm to analyze and map the keyword frequencies of your data. ... -
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ConvNetJS
Deep learning in Javascript to train convolutional neural networks
...It currently supports common Neural Network modules (fully connected layers, non-linearities), classification (SVM/Softmax) and Regression (L2) cost functions, ability to specify and train Convolutional Networks that process images, and experimental Reinforcement Learning modules, based on Deep Q Learning. The library allows you to formulate and solve Neural Networks in Javascript. If you would like to add features to the library, you will have to change the code in src/ and then compile the library into the build/ directory. ... -
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BudgetedSVM
BudgetedSVM: A C++ Toolbox for Large-scale, Non-linear Classification
We present BudgetedSVM, a C++ toolbox containing highly optimized implementations of three recently proposed algorithms for scalable training of Support Vector Machine (SVM) approximators: Adaptive Multi-hyperplane Machines (AMM), Budgeted Stochastic Gradient Descent (BSGD), and Low-rank Linearization SVM (LLSVM). BudgetedSVM trains models with accuracy comparable to LibSVM in time comparable to LibLinear, as it allows solving highly non-linear classi fication problems with millions of... -
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Bermuda Text-to-Speech
This project includes basic NLP and DSP techniques for Text-to-Speech
...page=tts This is an entirely written in JAVA project which includes a set of tools and methods designed to enable Multilingual Text-to-Speech (TTS) synthesis. We currently support English and Romanian but we will soon train more models and make them available for download. If you want to read more about our other NLP and TTS tools check out http://nlptools.racai.ro. -
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HSSVM(Hyper-Sphere Support Vector Machines) is a software for solving multi-classification problem, implemented by Java.
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SVM# is a svm(support vector machine) classification implemented in C#. The project contains both train and predict modules.
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Yann is Yet Another Neural Network. Yann is a library to create fast neural networks. It is also a GUI to easily create, edit, train, execute and investigate networks. Multiple topologies, runtime properties and ensemble learning are supported.
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With miao3d you can train a specific Gaussian Markov Random Field (GMRF) that then can be used to estimate a depthmap ("3D"), given an image ("2D"). A GUI allows inspection of the image + depthmap.
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Qt Handwriting Recognizing it's a simple Qt GUI interface of a artificial neural network to provide handwrite recognition. This project use FANN (Fast Artificial Neural Network) on first approach.
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A packet dissector driven by machine learning algorithms. You train it to recognize specific types of packets by showing it examples and counterexamples of some packet type, and it will figure out which bits in the packet define it as the type you seek.
