Practical Machine Learning with PyTorch (Part 1) скачать в хорошем качестве

Practical Machine Learning with PyTorch (Part 1)

The key learning objective from this workshop could be simply summarised as: Provide the ability to develop ML models in PyTorch. However, more specifically we aim to: provide an understanding of the structure of a PyTorch model and ML pipeline, introduce the different functionalities PyTorch might provide, encourage good research software engineering (RSE) practice, and exercise careful consideration and understanding of data used for training ML models. With regards to specific ML content we cover: using ML for both classification and regression, artificial neural networks (ANNs) and convolutional neural networks (CNNs) treatment of both tabular and image data.