Детектирование аномалий — интересная задача машинного обучения. Не существует какого-то определенного способа ее решения, так как каждый набор данных имеет свои особенности. Но в то же время есть...
YOLO-NAS Pose Модели YOLO-NAS Pose это последний вклад в область оценки позы. Ранее в этом году Deci получила широкое признание за свою новаторскую базовую модель обнаружения объектов...
1 code implementation in PyTorch. Model Stealing (MS) attacks allow an adversary with black-box access to a Machine Learning model to replicate its functionality, compromising the confidentiality of the model. Such attacks train a clone model by using the predictions of the target model for different inputs. The effectiveness of such attacks relies heavily on the availability of data necessary to query the target model. Existing attacks either assume partial access to the dataset of the target model or ava
1 code implementation in PyTorch. During the COVID-19 pandemic, there has been an emerging need for rapid, dedicated, and point-of-care COVID-19 patient disposition techniques to optimize resource utilization and clinical workflow. In view of this need, we present COVID-MobileXpert: a lightweight deep neural network (DNN) based mobile app that can use chest X-ray (CXR) for COVID-19 case screening and radiological trajectory prediction. We design and implement a novel three-player knowledge transfer and dis
1 code implementation in PyTorch. Hierarchical abstraction and curiosity-driven exploration are two common paradigms in current reinforcement learning approaches to break down difficult problems into a sequence of simpler ones and to overcome reward sparsity. However, there is a lack of approaches that combine these paradigms, and it is currently unknown whether curiosity also helps to perform the hierarchical abstraction. As a novelty and scientific contribution, we tackle this issue and develop a method
3 code implementations in TensorFlow and PyTorch. Existing aspect based sentiment analysis (ABSA) approaches leverage various neural network models to extract the aspect sentiments via learning aspect-specific feature representations. However, these approaches heavily rely on manual tagging of user reviews according to the predefined aspects as the input, a laborious and time-consuming process. Moreover, the underlying methods do not explain how and why the opposing aspect level polarities in a user review
This week on KDnuggets: Getting Started in 5 Steps series to help you master Python, SQL, Scikit-learn, PyTorch, and Google Cloud Platform • Unlock the Power of SQL in Data Visualization • And much, much more!
Lightning AI, the company behind PyTorch Lightning, with over 91 million downloads, announced the introduction of Lightning AI Studios, the culmination of 3 years of research into the next generation development paradigm for the age of AI.
To install PyTorch in Anaconda, open the Anaconda Prompt> create and activate the conda environment for PyTorch> use “conda install” command for installation.
To convert any image to grayscale in PyTorch, first, import libraries. Then, upload the image and read it. Next, use the “Grayscale()” transformation method.
To adjust the saturation of an image in PyTorch, use the “adjust_saturation()” method and provide the input image and saturation factor as an argument.
To adjust the brightness of an image in PyTorch, use the “adjust_brightness()” method and provide the input image and brightness factor as an argument.
This tutorial provides an in-depth introduction to machine learning using PyTorch and its high-level wrapper, PyTorch Lightning. The article covers essential steps from installation to advanced topics, offering a hands-on approach to building and training neural networks, and emphasizing the benefits of using Lightning.