Learn deep learning for image classification using Python to guide you as the Computer Vision and Deep Learning research advances. Read more.
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:
- Deep learning for computer vision with Python
- 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.
Get started today!
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 | ||
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:
- Deep learning for computer vision with Python
- 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.
Get started today!
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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