Machine Learning with PyTorch

SpiralTrain BV

In the course Machine Learning with PyTorch from SpiralTrain data scientists, data engineers and aspiring machine learning practitioners learn how to harness the power of the PyTorch framework to crea

Price

from €1,999

Level

Not specified

Kind

Training

Subject

Automation and IT, Training and education

More information and registration at SpiralTrain BV

Description

Intro PyTorch The course Machine Learning with PyTorch starts with an introduction to PyTorch, covering the basic principles of tensors, autograd and the PyTorch ecosystem. Linear Regression Subsequently linear regression in PyTorch for predicting results is discussed, including optimization with gradient descent, loss functions, regularization techniques and evaluation metrics. Neural Networks Then neural networks with PyTorch are treated, where activation functions, backpropagation and optimization algorithms are explained. Classification Classification tasks in PyTorch are also covered with logistic regression and cross entropy losses. Both binary and multi-class classification are treated. Model Building And model building is also on the program of the course Machine Learning with PyTorch. Here it is explained how more complex models can be based on fundamental building blocks, using feature engineering, categorical variables and hyperparameter tuning. Natural Language Processing Then Natural Language Processing with PyTorch is explained. The use of text classification, named entity recognition and sequence to sequence models for machine translations is covered.