"The initial setup is quite simple."
"The features for model comparison, the feature for model testing, evaluation, and deployment are very nice. It can work almost with all the models."
"The inferencing and processing capabilities are quite beneficial for our requirements."
"I like that PyTorch actually follows the pythonic way, and I feel that it's quite easy. It's easy to find compared to others who require us to type a long paragraph of code."
"Its interface is the most valuable. The ability to have an interface to train machine learning models and construct them with the high-level interface, without excess busting and reconstructing the same technical elements, is very useful."
"It has some disadvantages because when you're working with very complex models, neural networks if OpenVINO cannot convert them automatically and you have to do a custom layer and later add it to the model. It is difficult."
"The model optimization is a little bit slow — it could be improved."
"At this point, the product could probably just use a greater integration with more machine learning model tools."
"I would like a model to be available. I think Google recently released a new version of EfficientNet. It's a really good classifier, and a PyTorch implementation would be nice."
"There is not enough documentation about some methods and parameters. It is sometimes difficult to find information."
OpenVINO toolkit quickly deploys applications and solutions that emulate human vision. Based on Convolutional Neural Networks (CNNs), the toolkit extends computer vision (CV) workloads across Intel hardware, maximizing performance. The OpenVINO toolkit includes the Deep Learning Deployment Toolkit (DLDT).
We've built this course as an introduction to deep learning. Deep learning is a field of machine learning utilizing massive neural networks, massive datasets, and accelerated computing on GPUs. Many of the advancements we've seen in AI recently are due to the power of deep learning. This revolution is impacting a wide range of industries already with applications such as personal voice assistants, medical imaging, automated vehicles, video game AI, and more.
In this course, we'll be covering the concepts behind deep learning and how to build deep learning models using PyTorch. We've included a lot of hands-on exercises so by the end of the course, you'll be defining and training your own state-of-the-art deep learning models.
OpenVINO is ranked 3rd in AI Development Platforms with 3 reviews while PyTorch is ranked 4th in AI Development Platforms with 2 reviews. OpenVINO is rated 8.6, while PyTorch is rated 9.0. The top reviewer of OpenVINO writes "Open-source, easy to integrate, and perfectly tailored to the Movidius chipset". On the other hand, the top reviewer of PyTorch writes "A highly user-friendly open-source machine learning library". OpenVINO is most compared with TensorFlow, Google Cloud AI Platform, Microsoft Azure Machine Learning Studio and Caffe, whereas PyTorch is most compared with Microsoft Azure Machine Learning Studio, Caffe, IBM Watson Machine Learning, TensorFlow and MXNet. See our OpenVINO vs. PyTorch report.
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