Udemy – Deep Learning Image Classification in PyTorch 2.0 2023-11

Udemy – Deep Learning Image Classification in PyTorch 2.0 2023-11 Downloadly IRSpace

Aug 16, 2025 - 06:57 Updated: Aug 17, 2025 - 19:32

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Udemy – Deep Learning Image Classification in PyTorch 2.0 2023-11

Deep Learning Image Classification in PyTorch 2.0 Course. This comprehensive course teaches you how to build advanced image classification systems using deep learning and the PyTorch 2.0 framework in Python 3. Throughout the course, you will learn the fundamentals of designing and training deep neural networks to detect objects in images. The material is presented in a step-by-step, hands-on manner, including installing essential libraries like Cuda and Cudnn to take advantage of GPU capabilities, working with Google Colab Notebook, and connecting it to Google Drive for data management. It also covers industry-standard techniques for data preparation and processing with the torchvision library, data augmentation with methods like resizing, rotating, and changing colors, and implementing a data pipeline with Data Loader. You will learn about different neural network architectures, including LeNet, VGG16, Inception v3, and ResNet50, and analyze each model layer by layer. Additionally, topics such as implementing training and inference pipelines, transfer learning for working with limited data, and displaying model results on images are covered. By the end of the course, you will be able to design and build efficient image classification models, a skill that is useful in various fields such as image processing and machine vision. This course is designed for those interested in deep learning, from beginners to experts, and provides the knowledge necessary to enter the field.

What you will learn:

How to prepare an image classification dataset.
How to process dataset using image_folder and by extending dataset class from torchvision.
How to prepare and test Data Pipeline.
Familiarity with Data Augmentation such as Resize, Cropping, ColorJitter, RandomHorizontalflip, RandomVerticalFlip, RandomRotation.
Understand the detailed architecture of LeNet, VGG16, Inception v3 and ResNet50 with complete block diagram.
How to train a model with less data through Transfer Learning.
Introduction to the Training Pipeline for training any image classification model.
Introduction to Inference Pipeline for displaying results.
Familiarity with the process of evaluating image classification models through Precision, Recall, F1 Score, and Accuracy.

Who is this course suitable for?

Python developers interested in Deep Learning.
Deep Learning enthusiasts who want to understand the architecture of image classification models such as ResNet, VGG, LeNet, Inception.
Deep Learning enthusiasts who want to learn the new features of PyTorch 2.0.
Deep Learning enthusiasts who are learning Computer Vision and want to train and evaluate different image classification models.
Deep Learning enthusiasts who want to learn how to build a custom image classification dataset.