本书是一本使用 PyTorch 进行机器学习和深度学习的综合指南!不仅涵盖了Python机器学习库和用机器学习库搭建神经网络模型的方法,还介绍了机器学习算法的数学理论、工作原理、使用方法、实现细节以及如何避免机器学习算法实现过程中的常见问题。 书中还涵盖了多种用于文本和图像分类的机器学习与深度学习方法,包括全连接神经
Learn key concepts used to build machine learning models with PyTorch. We'll train a neural network model that recognizes and classifies images. Learning objectives In this module you will: Learn how to use Tensors with CPUs and GPUs
Best Course It is the Best Course for Machine Learning! Sir has been like always has such important & difficult concepts of ML with such ease and great examples, Just amazing! Helpful 2 years ago Had a wonderfull experience and great use of knowledge. had a great time with you guys Thank...
Machine Learning with PyTorch and Scikit-Learnhas been a long time in the making, and I am excited to finally get to talk about the release of my new book. Initially, this project started as the 4th edition ofPython Machine Learning. However, we made so many changes to the book that we...
Machine Learning with PyTorch and Scikit-Learn by Sebastian Raschka 星级: 323 页 Sebastian Raschka, Vahid Mirjalili-Python Machine Learning. Machine Learning and Deep Learning with Python, scikit-learn and Ten 星级: 606 页 Python Machine Learning Machine Learning and Deep Learning with Python,...
Take Udacity's Introduction to Pytorch Machine Learning course and learn foundational machine learning algorithms, starting with data cleaning and supervised models.
Course learning goals: Explore advanced data science; train models; examine result; recognize data bias IBM: Machine Learning with Python course Data scientists from IBM guide students through machine learning algorithms, Python classifications techniques, and data regressions. Participants are recommended ...
Machine Learning with PyTorch and Scikit-Learn is a comprehensive gui... (展开全部) 作者简介 ··· Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian...
Yuxi (Hayden) Liu was a Machine Learning Software Engineer at Google. With a wealth of experience from his tenure as a machine learning scientist, he has applied his expertise across data-driven domains and applied his ML expertise in computational advertising, cybersecurity, and information retriev...
Applying Machine Learning to Sentiment Analysis Predicting Continuous Target Variables with Regression Analysis Working with Unlabeled Data – Clustering Analysis Implementing a Multi-layer Artificial Neural Network from Scratch Parallelizing Neural Network Training with PyTorch ...