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**AD-TEAM** · 05-03-2025, 11:31 PM

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Deep Learning Image Classification In Pytorch 2.0
Published 11/2023
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.68 GB | Duration: 4h 26m

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

What you'll 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

Description

Welcome to this Deep Learning Image Classification course with PyTorch2.0 in Python3. Do you want to learn how to create powerful image classification recognition systems that can identify objects with immense accuracy? if so, then this course is for you what you need! In this course, you will embark on an exciting journey into the world of deep learning and image classification. This hands-on course is designed to equip you with the knowledge and skills necessary to build and train deep neural networks for the purpose of classifying images using the PyTorch framework.We have divided this course into Chapters. In each chapter, you will be learning a new concept for training an image classification model. These are some of the topics that we will be covering in this course:Training all the models with torch.compile which was introduced recently in Pytroch2.0 as a new feature.Install Cuda and Cudnn libraires for PyTorch2.0 to use GPU. How to use Google Colab Notebook to write Python codes and execute code cell by cell.Connecting Google Colab with Google Drive to access the drive data.Master the art of data preparation as per industry standards. Data processing with torchvision library. data augmentation to generate new image classification data by using:- Resize, Cropping, RandomHorizontalFlip, RandomVerticalFlip, RandomRotation, and ColorJitter.Implementing data pipeline with data loader to efficiently handle large datasets.Deep dive into various model architectures such as LeNet, VGG16, Inception v3, and ResNet50.Each model is explained through a nice block diagram through layer by layer for deeper understanding.Implementing the training and Inferencing pipeline.Understanding transfer learning to train models on less data.Display the model inferencing result back onto the image for visualization purposes. By the end of this comprehensive course, you'll be well-prepared to design and build image classification models using deep learning with PyTorch2.0. These skills will open doors to a wide range of applications, from classifying everyday objects to solving complex image analysis problems in various industries. Whether you're a beginner or an experienced data scientist, this course will equip you with the knowledge and practical experience to excel in the field of deep learning(Computer Vision).Feel Free to message me on the Udemy Ques and Ans board, if you have any queries about this Course. We give you the best reply in the shortest time as soon as possible.Thanks for checking the course Page, and I hope to see you in my course.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 what is PyTorch ?

Lecture 3 Installing Python and PyCharm

Lecture 4 Installing Cuda Libraries

Lecture 5 Installing PyTorch Package

Lecture 6 Introduction to Colab

Section 2: Data Preparation and Processing in PyTorch

Lecture 7 About Dataset Preparation

Lecture 8 Data Processing with ImageFolder

Lecture 9 Implementation of Data Processing with ImageFolder

Lecture 10 Data Processing with Custom Class

Lecture 11 Implementation of Data Processing with Custom Class

Lecture 12 Testing Data Preparation Pipeline

Section 3: LeNet

Lecture 13 LeNet-5 Architecture

Lecture 14 Implementation of LeNet Model

Lecture 15 Issue while Training Model on Colab

Lecture 16 Training LeNet Model in PyTorch 2.0 Part1

Lecture 17 Training LeNet Model in PyTorch 2.0 Part2

Lecture 18 Inferencing LeNet Model in PyTorch 2.0

Section 4: VGG 16 Model

Lecture 19 VGG 16 Model Architecture

Lecture 20 Implementation of VGG 16 Model

Lecture 21 Training VGG 16 Model in PyTorch 2.0

Lecture 22 Inferencing VGG 16 Model in PyTorch 2.0

Section 5: Inception Model

Lecture 23 Inception Model Architecture

Lecture 24 Implementation of Inception Model

Lecture 25 Training Inception Model in PyTorch 2.0

Lecture 26 Inferencing Inception Model in PyTorch 2.0

Section 6: ResNet Model

Lecture 27 ResNet 50 Model Architecture

Lecture 28 Implementation of ResNet 50 Model

Lecture 29 Training ResNet Model in PyTorch 2.0

Lecture 30 Inferencing ResNet Model in PyTorch 2.0

Section 7: Conclusion

Lecture 31 Thank You

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