Deep Learning Image Classification in PyTorch 2.0

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Last updated on December 16, 2024 12:17 am
Deep Learning Image Classification in PyTorch 2.0
Deep Learning Image Classification in PyTorch 2.0

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Deep Learning Image Classification in PyTorch 2.0

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$44.99  in stock
Udemy.com
as of December 16, 2024 12:17 am

Deep Learning | Computer Vision | Image Classification Model Training and Testing | PyTorch 2.0 | Python3

Created by: Pooja Dhouchak
Founder and CEO of FatheVision
Created by: FatheVision AI
Now Computer Vision is easy to learn
Rating:4.89 (40reviews)     175students enrolled

What Will I Learn?

  • Learn to prepare an image classification dataset.
  • Learn to process the dataset by using image_folder and by extending the dataset class from torchvision.
  • Learn to prepare and test the data pipeline.
  • Learning about Data augmentation such as resize, cropping, ColorJitter, RandomHorizontalflip, RandomVerticalFlip, RandomRotation.
  • Understanding the detail architecture of LeNet, VGG16, Inception v3, and ResNet50 with complete block diagram.
  • Learn to train the model on less data through transfer learning.
  • Learning about training pipeline to train any image classification model.
  • Learning about inference pipeline to display the result.
  • Learning about evalution process of image classification model through Precision, Recall, F1 Score, and Accuracy.

Requirements

  • Basic knowledge of Python
  • Access to internet connection
  • Basic understanding of CNNs

Target audience

  • Python developer who is interested in Deep Learning
  • Deep Learning enthusiasts who wants to understand Architecture of Image Classification Models such ResNet, VGG, LeNet, Inception
  • Deep Learning enthusiasts who wants to learn new features of PyTorch 2.0.
  • Deep Learning enthusiasts who is learning Computer Vision and wants to train and evaluate various image classification models
  • Deep Learning enthusiasts who wants to learn how to build an custom image classification data

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