ISBN:
9781801817356
,
1801817359
Sprache:
Unbestimmte Sprache
Seiten:
1 online resource (1 video file)
DDC:
006.3/2
Schlagwort(e):
Neural networks (Computer science)
;
Machine learning
;
Python (Computer program language)
;
Réseaux neuronaux (Informatique)
;
Apprentissage automatique
;
Python (Langage de programmation)
;
Instructional films
;
Nonfiction films
;
Internet videos
;
Films de formation
;
Films autres que de fiction
;
Vidéos sur Internet
Kurzfassung:
Image segmentation is a key technology in the field of computer vision, which enables computers to understand the content of an image at a pixel level. It has numerous applications, including autonomous vehicles, medical imaging, and augmented reality. You will start by exploring tensor handling, automatic gradient calculation with autograd, and the fundamentals of PyTorch model training. As you progress, you will build a strong foundation, covering critical topics such as working with datasets, optimizing hyperparameters, and the art of saving and deploying your models. With a robust understanding of PyTorch, you will dive into the heart of the course--semantic segmentation. You will explore the architecture of popular models such as UNet and FPN, understand the intricacies of upsampling, grasp the nuances of various loss functions, and become fluent in essential evaluation metrics. Moreover, you will apply this knowledge in real-world scenarios, learning how to train a semantic segmentation model on a custom dataset. This practical experience ensures that you are not just learning theory but gaining the skills to tackle actual projects with confidence. By course end, you will wield the power to perform multi-class semantic segmentation on real-world datasets. What You Will Learn Implement multi-class semantic segmentation with PyTorch Explore UNet and FPN architectures for image segmentation Understand upsampling techniques and their importance in deep learning Learn the theory behind loss functions and evaluation metrics Perform efficient data preparation to reshape inputs to the appropriate format Create a custom dataset class for image segmentation in PyTorch Audience This course is tailored to a diverse audience, making it accessible to both newcomers and experienced individuals in the field of computer vision. If you are an aspiring developer eager to delve into image segmentation or a data scientist aiming to expand your deep learning repertoire, this course is for you. While no prior image segmentation knowledge is required, a fundamental understanding of Python is essential. Familiarity with machine learning concepts will be beneficial. About The Author Bert Gollnick: Bert Gollnick is a proficient data scientist with substantial domain knowledge in renewable energies, particularly wind energy. With a rich background in aeronautics and economics, Bert brings a unique perspective to the field. Currently, Bert holds a significant role at a leading wind turbine manufacturer, leveraging his expertise to contribute to innovative solutions. For several years, Bert has been a dedicated instructor, offering comprehensive training in data science and machine learning using R and Python. The core interests of Bert lie at the crossroads of machine learning and data science, reflecting a commitment to advancing these disciplines.
Anmerkung:
Machine-generated record
