Network Engineer Salary: Your 2025 Guide
January 6, 2025
Article
Instructor: Parth Dhameliya
18,887 already enrolled
Included with
(201 reviews)
Recommended experience
Intermediate level
Prior programming experience in Python and basic pytorch. Theoretical knowledge of Convolutional Neural Network and Training process (Optimization)
(201 reviews)
Recommended experience
Intermediate level
Prior programming experience in Python and basic pytorch. Theoretical knowledge of Convolutional Neural Network and Training process (Optimization)
Use U-Net architecture for segmentation
Create train function and evaluator for training loop
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Only available on desktop
In this 2-hour project-based course, you will be able to :
- Understand the Segmentation Dataset and you will write a custom dataset class for Image-mask dataset. Additionally, you will apply segmentation augmentation to augment images as well as its masks. For image-mask augmentation you will use albumentation library. You will plot the image-Mask pair. - Load a pretrained state of the art convolutional neural network for segmentation problem(for e.g, Unet) using segmentation model pytorch library. - Create train function and evaluator function which will helpful to write training loop. Moreover, you will use training loop to train the model.
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Set up colab runtime environment
Setup Configurations
Augmentations
Custom Dataset
Load Dataset into batches
Create Segmentation Model
Create Train and Eval Function
Train Model
Inference
Prior programming experience in Python and basic pytorch. Theoretical knowledge of Convolutional Neural Network and Training process (Optimization)
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The Coursera Project Network is a select group of instructors who have demonstrated expertise in specific tools or skills through their industry experience or academic backgrounds in the topics of their projects. If you're interested in becoming a project instructor and creating Guided Projects to help millions of learners around the world, please apply today at teach.coursera.org.
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Available only on desktop
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201 reviews
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24.37%
7.46%
1.99%
4.97%
Showing 3 of 201
Reviewed on Feb 20, 2022
Great instructor and very practical hands-on approach. I would prefer more explanation on other encoder and weight presets as that will be important for transferring the knowledge learned here!
Reviewed on May 5, 2023
Very nicely taught. Code must be made available in text file.
Reviewed on Jul 20, 2023
There are some typos and some things that are not shown in the video but if you worked with python before you should be able to solve them easily. Overall a great exercise.
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