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Hacking Business Training With Microlearning | HackerNoon

A look at how microlearning is changing the game to ramp up workplace training.

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Semi-supervised adversarial text generation based on seq2seq models - Amazon Science

To improve deep learning models’ robustness, adversarial training has been frequently used in computer vision with satisfying results. However, adversarial perturbation on text have turned out to be more challenging due to the discrete nature of text. The generated adversarial text might not sound…

MongoDB Course Training Online - KnowBigData

MongoDB Course Training Online from KnowBigData introduced to MongoDB which is a robust, mature, open source NoSql Databases

Training Data to Employ AI in Healthcare - DataScienceCentral.com

AI in healthcare for machine learning data. We are providing a best accuracy of data within the modern technology

Runway Introduces Custom AI Training

Now, you'll be able to "turn anything into everything" by training your custom AI model with just a couple of photos.

8 Ways To Improve Customer Training By Employing Salesforce LMS | Tech News | Startups News

TechStartups - Coverage of Technology News, technology startups, Emerging technology, venture capital funding, and Silicon Valley

How MIT is training AI language models in an era of quality data scarcity | VentureBeat

As more sophisticated AI language models are developed, more quality data is needed for training them. MIT is working to solve this issue.

Как я могу получить данные для колчана в факеле? – 1 Ответ

Вы можете вычислить градиенты torch.Tensor путем прохождения torch.Tensor единиц. import... Вопрос по теме: python, pytorch.

NUBIA: NeUral Based Interchangeability Assessor for Text Generation | Papers With Code

1 code implementation in PyTorch. We present NUBIA, a methodology to build automatic evaluation metrics for text generation using only machine learning models as core components. A typical NUBIA model is composed of three modules: a neural feature extractor, an aggregator and a calibrator. We demonstrate an implementation of NUBIA which outperforms metrics currently used to evaluate machine translation, summaries and slightly exceeds/matches state of the art metrics on correlation with human judgement on t

LiDAR guided Small obstacle Segmentation | Papers With Code

2 code implementations in PyTorch. Detecting small obstacles on the road is critical for autonomous driving. In this paper, we present a method to reliably detect such obstacles through a multi-modal framework of sparse LiDAR(VLP-16) and Monocular vision. LiDAR is employed to provide additional context in the form of confidence maps to monocular segmentation networks. We show significant performance gains when the context is fed as an additional input to monocular semantic segmentation frameworks. We furth

Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation | Papers With Code

1 code implementation in PyTorch. We address the problem of semantic nighttime image segmentation and improve the state-of-the-art, by adapting daytime models to nighttime without using nighttime annotations. Moreover, we design a new evaluation framework to address the substantial uncertainty of semantics in nighttime images. Our central contributions are: 1) a curriculum framework to gradually adapt semantic segmentation models from day to night through progressively darker times of day, exploiting cross

ACDC (Adverse Conditions Dataset with Correspondences) Dataset | Papers With Code

We introduce ACDC, the Adverse Conditions Dataset with Correspondences for training and testing semantic segmentation methods on adverse visual conditions. It comprises a large set of 4006 images which are evenly distributed between fog, nighttime, rain, and snow. Each adverse-condition image comes with a high-quality fine pixel-level semantic annotation, a corresponding image of the same scene taken under normal conditions and a binary mask that distinguishes between intra-image regions of clear and uncer

Loft Dynamics raises $20M to tackle pilot shortage with VR training • TechCrunch

Loft Dynamics, a Swiss startup creating virtual reality (VR) simulation technology for helicopter pilots, has raised $20 million.

PyTorch 2.0 release accelerates open-source machine learning | VentureBeat

The new PyTorch 2.0 promises to accelerate ML training and development, while being backward-compatible with existing PyTorch code.

Meta-learning the difference: Preparing large language models for efficient adaptation - Amazon Science

Large pretrained language models (PLMs) are often domain- or task-adapted via finetuning or prompting. Finetuning requires modifying all of the parameters and having enough data to avoid overfitting while prompting requires no training and few examples but limits performance. Instead, we prepare…

This AI Method from MIT and IBM Research Improves the Training and Inference Performance of Deep Learning Models on Large Graphs - MarkTechPost

This AI Method from MIT and IBM Research Improves the Training and Inference Performance of Deep Learning Models on Large Graphs

PyTorch 2.0 release accelerates open-source machine learning | VentureBeat

The new PyTorch 2.0 promises to accelerate ML training and development, while being backward-compatible with existing PyTorch code.

Machine Learning: AWS Brings Data Training to Community, Historically Black Colleges - The New Stack

AWS wants to help close the gap between the curriculum between elite four year universities and community colleges, MSIs, and HBCUs when it comes to database, AI, and ML.

Gift a Lifetime of Software and IT Training for $59 | PCMag

Access over 110 courses, 6,500+ individual lessons, and 800+ hours of training.

Getting Started with PyTorch Lightning - KDnuggets

Introduction to PyTorch Lightning and how it can be used for the model building process. It also provides a brief overview of the PyTorch characteristics and how they are different from TensorFlow.

Элементарный слой фильтра PyTorch – 1 Ответ

В pytorch вы всегда можете реализовать свои собственные слои, сделав их подклассами nn.Module. Вы... Вопрос по теме: python, python-3.x, neural-network, pytorch.

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