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The de-facto approach to many vision tasks is to start from pretrained visual representations, typically learned via supervised training on ImageNet. Recent methods have explored unsupervised pretraining to scale to vast quantities of unlabeled images.
Now, we focus on the real purpose of PyTorch. Since it is mainly a deep learning framework, PyTorch provides a number of ways to create different types of neural networks. In this article, we create two types of neural networks for image classification.
Modern text-to-speech synthesis pipelines typically involve multiple processing stages, each of which is designed or learnt independently from the rest. In this work, we take on the challenging task of learning to synthesise speech from normalised text or phonemes in an end-to-end manner, resulting in models which operate directly on character or phoneme input sequences and produce raw speech audio outputs.
Learn how to build and deploy a machine learning application from scratch. An end-to-end tutorial on data scraping, modeling, and deployment.
A list of NLP(Natural Language Processing) tutorials built on PyTorch.