Deep Learning with Python for Image Classification

Learn Deep Learning, Machine Learning & Computer Vision for Image Classification in PyTorch using CNN Transfer Learning

Created by : Rated :4 988 views

Last Updated: 2023-02-27 06:02:35 English Course Type: Self Learning

Learn Image Classification using Deep Learning PreTrained Models
Learn Single-Label Image Classification and Multi-Label Image Classification
Learn Deep Learning Architectures Such as ResNet and AlexNet
Write Python Code in Google Colab
Connect Colab with Google Drive and Access Data
Perform Data Preprocessing using Transformations
Perform Single-Label Image Classification with ResNet and AlexNet
Perform Multi-Label Image Classification with ResNet and AlexNet
Learn Transfer Learning
Dataset, Data Augmentation, Dataloaders, and Training Function
Deep ResNet Model FineTuning
ResNet Model HyperParameteres Optimization
Deep ResNet as Fixed Feature Extractor
Models Optimization, Training and Results Visualization

Are you interested in unlocking the full potential of Artificial Intelligence? Do you want to learn how to create powerful image recognition systems that can identify objects with incredible accuracy? If so, then our course on Deep Learning with Python for Image Classification is just what you need! In this course, you will learn Deep Learning with Python and PyTorch for Image Classification using Pre-trained Models and Transfer Learning. Image Classification is a computer vision task to recognize an input image and predict a single-label or multi-label for the image as output using Machine Learning techniques.

You will use Google Colab notebooks for writing the python code for image classification using Deep Learning models.
You will learn how to connect Google Colab with Google Drive and how to access data.
You will perform data preprocessing using different transformations such as image resize and center crop etc.
You will perform two types of Image Classification, single-label Classification, and multi-label Classification using deep learning models with Python.
You will be able to learn Transfer Learning techniques:

1. Transfer Learning by FineTuning the model.

2. Transfer Learning by using the Model as Fixed Feature Extractor.
You will learn how to perform Data Augmentation.
You will learn how to load Dataset, Dataloaders.
You will Learn to FineTune the Deep Resnet Model.
You will learn how to use the Deep Resnet Model as Fixed Feature Extractor.
You will Learn HyperParameters Optimization and results visualization.

In single-label Classification, when you feed input image to the network it predicts single label. In multi-label Classification, when you feed input image to the network it predicts multiple labels. You will Learn Deep Learning architectures such as ResNet and AlexNet. The ResNet is a deep convolution neural network proposed for image classification and recognition. ResNet network architecture designed for classiﬁcation task, trained on the imageNet dataset of natural scenes that consists of 1000 classes. Deep residual nets won the 1st place on the ILSVRC 2015 Classification challenge. Alexnet is a deep convolution neural network trained on ImageNet dataset to classify the images into 1000 classes. It has five convolution layers followed by max-pooling layers, and 3 fully connected layers. AlexNet won the ILSVRC 2012 Classification challenge. You will perform image classification using ResNet and AlexNet deep learning models. The Deep Learning community has greatly benefitted from these open-source models where pre-trained models are a major reason for rapid advancements in the Computer Vision and deep learning research.

Deep Learning with Python and Pytorch is taught in this course
A Google Gmail account to get started with Google Colab to write Python Code

Deep Learning enthusiasts interested to learn with Python and Pytorch
Students and researchers interested in Deep Learning for Image Classification

Section 1 : Introduction to the Course 1 Lectures 00:02:22
Lecture 1 :
Introduction to the Course Preview
Section 2 : Define Image Classification 1 Lectures 00:02:59
Lecture 1 :
Image Classification with single label and multi-label
Section 3 : Pretrained Models Definition 1 Lectures 00:05:11
Lecture 1 :
PreTrained Models and their Applications
Section 4 : Deep Learning Architectures for Image Classification 1 Lectures 00:04:58
Lecture 1 :
Deep Learning ResNet and AlexNet Architectures for Image Classification
Section 5 : Google Colab for Writing Python Code 1 Lectures 00:05:11
Lecture 1 :
Set-up Google Colab for Writing Python Code
Section 6 : Connect Google Colab with Google Drive 1 Lectures 00:02:43
Lecture 1 :
Connect Google Colab with Google Drive to Read and Write Data
Section 7 : Access Data from Google Drive to Colab 1 Lectures 00:02:44
Lecture 1 :
Read Data from Google Drive to Colab Notebook
Section 8 : Data Preprocessing for Image Classification 1 Lectures 00:05:00
Lecture 1 :
Perform Data Preprocessing for Image Classification
Section 9 : Single-Label Image Classification using Deep Learning Models 2 Lectures 00:08:03
Lecture 1 :
Single-Label Image Classification using ResNet and AlexNet PreTrained Models
Lecture 2 :
Resources: Single-Label Classification
Section 10 : Multi-Label Image Classification using Deep Learning Models 2 Lectures 00:06:21
Lecture 1 :
Multi-Label Image Classification using ResNet and AlexNet PreTrained Models
Lecture 2 :
Resources: Multi-Label Classification
Section 11 : Transfer Learning 1 Lectures 00:06:09
Lecture 1 :
Introduction to Transfer Learning
Section 12 : Link Google Drive with Google Colab 1 Lectures 00:02:43
Lecture 1 :
Link Google Drive with Google Colab
Section 13 : Dataset, Data Augmentation, Dataloaders, and Training Function 1 Lectures 00:07:20
Lecture 1 :
Dataset, Data Augmentation, Dataloaders, and Training Function
Section 14 : Deep ResNet Model FineTuning 1 Lectures 00:06:22
Lecture 1 :
ResNet Model HyperParameteres Optimization
Section 15 : Deep ResNet Training 1 Lectures 00:03:34
Lecture 1 :
Deep ResNet Model Training
Section 16 : Deep ResNet Feature Extractor 1 Lectures 00:04:42
Lecture 1 :
Deep ResNet as Fixed Feature Extractor
Section 17 : Model Optimization, Training and Results 1 Lectures 00:05:51
Lecture 1 :
Model Optimization, Training and Results Visualization
Section 18 : Resources: Code for Transfer Learning by FineTuning and Model Feature Extractor 3 Lectures 00:01:41
Lecture 1 :
Code of Classification using Transfer Learning
Lecture 2 :
Code for Transfer Learning by FineTuning and Model Feature Extractor
Lecture 3 :
Classification Dataset

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Mazhar Hussain,

1830 Course Views

2 Courses

Mazhar Hussain is currently in the role of Deep Learning and Computer Vision Engineer. He has extensive teaching experience at University Higher Education level and Online over a decade. He has published several research papers on Deep Learning in well-reputed Journals and Conferences. He believes on comprehensive practical trainings with stunning support for his students where all his courses are 100% hands-on with step-by-step problem-based learning, demos and examples. Mazhar Hussain is teaching Computer Science courses at the National University of Computer and Emerging Sciences and Online since a decade. He has been teaching courses in: · Artificial Intelligence (AI) · Machine Learning (ML) · Deep Learning (DL) · Computer Vision (CV) · Data Science (DS) · Programming (Python, C++, Java) · Databases especially in SQL SERVER, MYSQL, ORACLE, and MS ACCESS He holds a Master's Degree in Computer Science and is passionate to deliver practical knowledge and skills to his students. He has worked as a developer in the Microsoft Innovation Center and is now taking all that he has learned to help you discover amazing career opportunities. Mazhar believes that courses should teach real life skills that are current and they should not waste a student's valuable time. His courses are the most comprehensive and well-explained option available out there. One must start with the foundation and build upon it to learn effectively. He promises that His approach allows for exponential learning. He believes that everyone has the potential to learn and excel, and He is dedicated to helping his students achieve their full potential. He is excited to share his knowledge and experience with you, and look forward to helping you achieve your goals. See you inside the courses! Please do not hesitate if you have any questions, He is always available for your help at any time to transform a passionate, enthusiastic learner into a skilled person.