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Deep Learning With Python For Image Classification
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6 STUDENTS
1h 28m

Learn to perform image classification using ResNet and AlexNet Deep Learning models.

Read more.
Course Skill Level
Intermediate
Time Estimate
1h 28m

Instructor

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 te


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About This Course

Who this course is for:

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

What you’ll learn:

  • Image classification using Deep Learning pretrained models
  • Single-label image classification and multi-label image classification
  • 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 fine tuning
  • ResNet model hyperparameteres optimization
  • Deep ResNet as fixed feature extractor
  • Models optimization, training and results visualization

Requirements:

  • 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

In this course, you will learn Deep Learning with Python and PyTorch for Image Classification using Pre-trained Models. 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 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 Computer Vision and Deep Learning research.

Our Promise to You
By the end of this course, you will have learned image classification in Python.

10 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.

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Course Curriculum

Section 1 - Introduction
Introduction To The Course 00:00:00
Section 2 - Image Classification, Pretrained Models, Deep Learning Architectures
Image Classification With Single Label And Multi-Label 00:00:00
Pretrained Models And Their Applications 00:00:00
Deep Learning Resnet And Alexnet Architectures For Image Classification 00:00:00
Section 3 - Google Colab
Set-up Google Colab For Writing Python Code 00:00:00
Connect Google Colab With Google Drive To Read And Write Data 00:00:00
Read Data From Google Drive To Colab Notebook 00:00:00
Section 4 - Image Classification
Perform Data Preprocessing For Image Classification 00:00:00
Single-Label Image Classification Using Resnet And Alexnet Pretrained Models 00:00:00
Python Code For Single-Label Classification 00:00:00
Multi-Label Image Classification Using Resnet And Alexnet Pretrained Models 00:00:00
Python Code For Multi-Label Classification 00:00:00
Section 5 - Transfer Learning
Introduction To Transfer Learning 00:00:00
Link Google Drive With Google Colab 00:00:00
Dataset, Data Augmentation, Dataloaders, And Training Function 00:00:00
Deep Resnet Model Finetuning 00:00:00
Resnet Model Hyperparameteres Optimization 00:00:00
Deep ResNet Model Training 00:00:00
Deep ResNet As Fixed Feature Extractor 00:00:00
Model Optimization, Training And Results Visualization 00:00:00
Section 6 - Resources: Code And Dataset Of Finetuning And Model Feature Extractor
Code Of Classification Using Transfer Learning 00:00:00
Code For Transfer Learning 00:00:00
Classification Data Set 00:00:00
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