Modern Computer Vision & Deep Learning with Python & PyTorch

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Computer Science & AI School,Mazhar Hussain

6:41:51

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1. Introduction

1. Introduction to Course.mp4

03:20

2. What is Computer Vision & its Applications

1. Introduction to Computer Vision and its Real-world Applications.mp4

11:08

2. Major Computer Vision Tasks.mp4

02:40

3. Deep Convolutional Neural Networks (CNN) for Computer Vision

1. Introduction to Convolutional Neural Networks (CNN).mp4

08:14

4. Setting-up Google Colab for Writing Python Code

1. Introduction to Google Colab for Python Coding.mp4

05:11

2. Connect Google Colab with Google Drive.mp4

02:43

5. Image Classification Task of Computer Vision

1. Introduction to Single and Multi-label Image Classification.mp4

02:59

6. Pretrained Models for Single and Multi-Label Image Classification

1. Introduction to Pretrained Models.mp4

05:11

2. Deep Learning ResNet and AlexNet Architectures.mp4

04:58

3. Access Data from Google Drive to Colab.mp4

02:44

4. Data Preprocessing for Image Classification.mp4

05:00

5. Single-Label Image Classification using ResNet and AlexNet PreTrained Models.mp4

08:03

6.1 Lecture 2 - Resources Single Label Classification.zip

6. Single Label Classification Python and Pytorch Code.html

7. Multi-Label Image Classification using Deep Learning Models.mp4

06:21

8.1 Lecture 2 - Resources Multi Label Classification.zip

8. Multi-Label Classification Python and PyTorch Code.html

7. Transfer Learning for Image Classification

1. Introduction to Transfer Learning.mp4

06:09

2. Dataset, Data Augmentation, and Dataloaders.mp4

07:20

3.1 Classification Dataset.zip

3. Dataset for Classification.html

4. FineTuning Deep ResNet Model.mp4

07:19

5. HyperParameteres Optimization for Model.mp4

06:22

6. Training Deep ResNet Model.mp4

03:34

7. Fixed Feature Extractraction using ResNet.mp4

04:42

8. Model Optimization, Training and Results Visualization.mp4

05:51

9.1 Code for Transfer Learning by FineTuning and Model Feature Extractor.zip

9. Complete Python Code for Transfer Learning and Dataset.html

8. Semantic Segmentation Task Of Computer Vision

1. Introduction to Semantic Image Segmentation.mp4

05:31

2. Semantic Segmentation Real-World Applications.mp4

11:02

9. Deep Learning Architectures For Segmentation (UNet, PSPNet, PAN)

1. Pyramid Scene Parsing Network (PSPNet) For Segmentation.mp4

04:30

2. UNet Architecture For Segmentation.mp4

03:37

3. Pyramid Attention Network (PAN).mp4

03:34

4. Multi-Task Contextual Network (MTCNet).mp4

04:37

10. Segmentation Datasets, Annotations, Data Augmentation & Data Loading

1. Datasets for Semantic Segmentation.mp4

05:52

2. Data Annotations Tool for Semantic Segmentation.mp4

05:29

3. Data Loading with PyTorch Customized Dataset Class.mp4

18:01

4. Data Loading for Segmentation with Python and PyTorch Code.html

5. Data Augmentation using Albumentations with Different Transformations.mp4

09:49

6. Augmentation Python Code.html

7. Learn To Implement Data Loaders In Pytorch.mp4

05:01

11. Performance Metrics (IOU) For Segmentation Models Evaluation

1. Performance Metrics (IOU, Pixel Accuracy, Precision, Recall, Fscore).mp4

09:48

2. Code (Python and PyTorch).html

12. Encoders and Decoders For Segmentation In PyTorch

1. Transfer Learning And Pretrained Deep Resnet Architecture.mp4

08:49

2. Encoders for Segmentation with PyTorch Liberary.mp4

09:38

3. Decoders for Segmentation in PyTorch Liberary.mp4

10:04

13. Implementation, Optimization and Training Of Segmentation Models

1. Implement Segmentation Models (UNet, PSPNet, DeepLab, PAN, and UNet++).mp4

17:00

2. Segmentation Models Code with Python.html

3. Learn To Optimize Hyperparameters For Segmentation Models.mp4

08:31

4. Model Optimaztion Code (Python And PyTorch).html

5. Training of Segmentation Models.mp4

09:00

6. Model Training Code (Python And PyTorch).html

14. Test Models and Visualize Segmentation Results

1. Test Models and Calculate IOU,Pixel Accuracy,Fscore.mp4

12:01

2. Test Models and Calculate Performance Scores (Python Code).html

3. Visualize Segmentation Results and Generate RGB Segmented Map.mp4

12:00

4. Segmentation Results Visualization (Python Code).html

15. Complete Code and Dataset for Semantic Segmentation

1. Final Code Review.mp4

03:19

2.1 Lecture 2 - Final Code.zip

2.2 Lecture 3 - TrayDataset for Segmentation.zip

2. Complete Code and Dataset is Attached.html

16. Object Detection Task Of Computer Vision

1. Object Detection and its Applications.mp4

07:39

17. Deep Learning Architectures for Object Detection (R-CNN Family)

1. Deep Convolutional Neural Network (VGG, ResNet, GoogleNet).mp4

08:18

2. RCNN Deep Learning Architectures for Object Detection.mp4

08:24

3. Fast RCNN Deep Learning Architectures for Object Detection.mp4

05:16

4. Faster RCNN Deep Learning Architectures for Object Detection.mp4

03:15

18. Detectron2 for Ojbect Detection

1. Detectron2 for Ojbect Detection with PyTorch.mp4

18:10

2. Perform Object Detection using Detectron2 Pretrained Models.mp4

10:41

3.1 object detection with detctron2.zip

3. Python and PyTorch Code.html

19. Training, Evaluating and Visualizing Object Detection on Custom Dataset

1. Custom Dataset for Object Detection.mp4

12:18

2.1 balloon.zip

2. Dataset for Object Detection.html

3. Train, Evaluate Object Detection Models & Visualizing Results on Custom Dataset.mp4

13:32

4.1 object detection on custom dataset.zip

4. Python and PyTorch Code.html

20. Complete Code and Custom Dataset for Object Detection

1.1 balloon.zip

1.2 Python and PyTorch Code.zip

1. Resources Code and Custom Dataset for Object Detection.html

21. Instance Segmentation Task of Computer Vision

1. What is Instance Segmentation.mp4

04:35

22. Mask RCNN for Instance Segmentation

1. Mask RCNN for Instance Segmentation.mp4

04:24

23. Training, Evaluating and Visualizing Instance Segmentation on Custom Dataset

1. Train, Evaluate Instance Segmentation Model & Visualizing Results on Custom Data.mp4

18:17

24. Complete Code and Custom Dataset for Instance Segmentation

1.1 balloon.zip

1.2 Instance Segmentation on Custom Dataset.zip

1. Resources Complete Code and Custom Dataset for Instance Segmentation.html

Description

Computer Vision with Python using Deep Learning for Classification, Instance & Semantic Segmentation, & Object Detection

What You'll Learn?

Learn Computer Vision and Deep Learning with Real-world Applications in Python
Learn Deep Convolutional Neural Networks (CNN) for Computer Vision
Computer Vision for Single and Multi-label Classification with Python and Pytorch
Computer Vision for Image Semantic Segmentation with Python and Pytorch
Computer Vision for Image Instance Segmentation with Python and Pytorch
Computer Vision for Object Detection with Python and Pytorch
Google Colab with GPU for Writing Python and Pytorch Code
Learn Data Augmentation with Different Image Transformations
Custom Datasets for Image Classification, Image Segmentation and Object Detection
Hyperparameters Optimization of Deep Learning Models to Improve Performance
Learn Performance Metrics (Accuracy, IOU, Precision, Recall, Fscore)
Transfer Learning with Pretrained Models of Deep Learning in Pytorch
Train Image Segmentation, Classification and Object Detection Models on Custom Datasets
Evaluate and Deploy Image Segmentation, Image Classification and Object Detection Models
Object Detection using Detectron2 Models Introduced by Facebook Artificial Intelligence Research (FAIR) Group
Perform Object Detection using RCNN, Fast RCNN, Faster RCNN Models with Python and Pytorch
Perform Semantic Segmentation with UNet, PSPNet, DeepLab, PAN, and UNet++ Models with Pytoch and Python
Perform Instance Segmentation using Mask RCNN on Custom Dataset with Pytorch and Python
Perform Image Single and Multi-label Classification using Deep Learning Models (ResNet, AlexNet) with Pytorch and Python
Visualization of Results, Datasets, and Complete Python/Pytorch Code is Provided for Classification, Segmentation, and Object Detection

Who is this for?

This course is designed for individuals who are interested in learning how to apply Deep Learning techniques to solve Computer Vision problems in real-world using the Python programming language and the PyTorch Deep Learning Framework

Computer Vision Engineers, Artificial Intelligence AI enthusiasts and Researchers who want to learn how to use Python adn PyTorch to build, train and deploy Deep Learning models for Computer Vision problems

Machine Learning Engineers, Deep Learning Engineers, and Data Scientists who want to apply Deep Learning to Computer Vision tasks

Developers, Graduates and Researchers who want to incorporate Computer Vision and Deep Learning capabilities into their projects

In general, the course is for Anyone who wants to learn how to use Deep Learning to extract meaning from visual data and gain a deeper understanding of the theory and practical applications of Computer Vision using Python and PyTorch

What You Need to Know?

Computer Vision and Deep Learning with Python and Pytorch is taught in this course by following a complete pipeline from Zero to Mastery

No prior knowledge of Computer Vision and Deep Learning is assumed. Everything will be covered with hands-on trainings

A Google Gmail account is required to get started with Google Colab to write Python and PytorchCode

More details

Description
Welcome to the course "Modern Computer Vision & Deep Learning with Python & PyTorch"! Imagine being able to teach computers to see just like humans. Computer Vision is a type of artificial intelligence (AI) that enables computers and machines to see the visual world, just like the way humans see and understand their environment. Artificial intelligence (AI) enables computers to think, where Computer Vision enables computers to see, observe and interpret. This course is particularly designed to provide a comprehensive, hands-on experience in applying Deep Learning techniques to major Computer Vision problems including Image Classification, Semantic Segmentation, Instance Segmentation, and Object Detection. In this course, you'll start with an introduction to the basics of Computer Vision and Deep Learning, and learn how to implement, train, test, evaluate and deploy your own models using Python and PyTorch for Image Classification, Image Segmentation, and Object Detection.
Computer Vision plays a vital role in the development of autonomous vehicles. It enables the vehicle to perceive and understand its surroundings to detect and classify various objects in the environment, such as pedestrians, vehicles, traffic signs, and obstacles. This helps to make informed decisions for safe and efficient vehicle navigation. Computer Vision is used for Surveillance and Security using drones to track suspicious activities, intruders, and objects of interest. It enables real-time monitoring and threat detection in public spaces, airports, banks, and other security-sensitive areas. Today Computer Vision applications in our daily life are very common including Face Detection in cameras and cell phones, logging in to devices with fingerprints and face recognition, interactive games, MRI, CT scans, image guided surgery and much more. This comprehensive course is especially designed to give you hands-on experience using Python and Pytorch coding to build, train, test and deploy your own models for major Computer Vision problems including Image Classification, Image Segmentation (Semantic Segmentation and Instance Segmentation), and Object Detection. So, are you ready to unleash the power of Computer Vision and Deep Learning with Python and PyTorch:
Master the cutting-edge techniques and algorithms driving the field of Computer Vision.
Dive deep into the world of Deep Learning and gain hands-on experience with Python and PyTorch, the industry-leading framework.
Discover the secrets behind building intelligent systems that can understand, interpret, and make decisions from visual data.
Unlock the power to revolutionize industries such as healthcare, autonomous systems, robotics, and more.
Gain practical skills through immersive projects, real-world applications, and hands-on coding exercises.
Gain insights into best practices, industry trends, and future directions in computer vision and deep learning.
What You'll Learn:
This course covers the complete pipeline with hands-on experience of Computer Vision tasks using Deep Learning with Python and PyTorch as follows:
Introduction to Computer Vision and Deep Learning with real-world applications
Learn Deep Convolutional Neural Networks (CNN) for Computer Vision
You will use Google Colab notebooks for writing the python code for image classification using Deep Learning models.
Perform data preprocessing using different transformations such as image resize and center crop etc.
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 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.
Semantic Image Segmentation and its Real-World Applications in Self Driving Cars or Autonomous Vehicles etc.
Deep Learning Architectures for Semantic Segmentation including Pyramid Scene Parsing Network (PSPNet), UNet, UNet++, Pyramid Attention Network (PAN),Â Multi-Task Contextual Network (MTCNet), DeepLabV3, etc.
Datasets and Data annotations Tool for Semantic Segmentation
Data Augmentation and Data Loading in PyTorch for Semantic Segmentation
Performance Metrics (IOU) for Segmentation Models Evaluation
Transfer Learning and Pretrained Deep Resnet Architecture
Segmentation Models Implementation in PyTorch using different Encoder and Decoder Architectures
Hyperparameters Optimization and Training of Segmentation Models
Test Segmentation Model and Calculate IOU, Class-wise IOU, Pixel Accuracy, Precision, Recall and F-score
Visualize Segmentation Results and Generate RGB Predicted Segmentation Map
Learn Object Detection using Deep Learning Models with Pytorch
Learn RCNN, Fast RCNN, Faster RCNN and Mask RCNN Architectures
Perform Object Detection with Fast RCNN and Faster RCNN
Introduction to Detectron2 by Facebook AI Research (FAIR)
Preform Object Detection with Detectron2 Models
Explore Custom Object Detection Dataset with Annotations
Perform Object Detection on Custom Dataset using Deep Learning
Train, Test, Evaluate Your Own Object Detection Models and Visualize Results
Perform Instance Segmentation using Mask RCNN on Custom Dataset with Pytorch and Python
Who Should Attend:
This course is designed for a wide range of students and professionals, including but not limited to:
Computer Vision Engineers, Artificial Intelligence AI enthusiasts and Researchers who want to learn how to use Python adn PyTorch to build, train and deploy Deep Learning models for Computer Vision problems
Machine Learning Engineers, Deep Learning Engineers, and Data Scientists who want to apply Deep Learning to Computer Vision tasks
Developers who want to incorporate Computer Vision and Deep Learning capabilities into their projects
Graduates and Researchers in Computer Science, Electrical Engineering, and other related fields who want to learn about the latest advances in Deep Learning for Computer Vision
In general, the course is for Anyone who wants to learn how to use Deep Learning to extract meaning from visual data and gain a deeper understanding of the theory and practical applications of Computer Vision using Python and PyTorch
This course is designed for AI enthusiasts, data scientists, software engineers, researchers, and anyone passionate about unlocking the potential of computer vision and deep learning. Whether you're a seasoned professional or just starting your journey, this course will equip you with the skills and knowledge needed to excel in this rapidly evolving field.
Join the Visionary Revolution:
Don't miss out on this incredible opportunity to join the visionary revolution in modern Computer Vision & Deep Learning. Expand your skill set, push the boundaries of innovation, and embark on a transformative journey that will open doors to limitless possibilities. By the end of this course, you'll have the knowledge and skills you need to start applying Deep Learning to Computer Vision problems including Image Classification, Image Segmentation, and Object Detection in your own work or research. Whether you're a Computer Vision Engineer, or Developer, this course is the perfect way to take your understanding of Deep Learning to the next level. Let's get started on this exciting journey of Deep Learning for Computer Vision with Python and PyTorch.
See you inside the Class!!
Who this course is for:
This course is designed for individuals who are interested in learning how to apply Deep Learning techniques to solve Computer Vision problems in real-world using the Python programming language and the PyTorch Deep Learning Framework
Computer Vision Engineers, Artificial Intelligence AI enthusiasts and Researchers who want to learn how to use Python adn PyTorch to build, train and deploy Deep Learning models for Computer Vision problems
Machine Learning Engineers, Deep Learning Engineers, and Data Scientists who want to apply Deep Learning to Computer Vision tasks
Developers, Graduates and Researchers who want to incorporate Computer Vision and Deep Learning capabilities into their projects
In general, the course is for Anyone who wants to learn how to use Deep Learning to extract meaning from visual data and gain a deeper understanding of the theory and practical applications of Computer Vision using Python and PyTorch

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Computer Science & AI School

Instructor's Courses

Computer Science & AI School aims to open the door to sought-after technology careers for you by learning cutting edge Computer Science courses in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Computer Vision (CV), Data Science (DS), Programming and Databases with course material ranges from entry-level to specialized topics. You will be able to update your marketable and competitive skills through commercial applications of computing practices. You’ll master in-demand computing skills, solve complex problems, and hone your innovation and creativity. The hands-on exercises and project-based approach will help develop the technical and transferable skills needed for a fulfilling career in your field.

Mazhar Hussain

Instructor's Courses

Mazhar Hussain is teaching Computer Science courses since 2015 at the National University of Computer and Emerging Sciences. He holds a Master's Degree in Computer Science and is passionate to deliver practical knowledge and skills to his students. He has been teaching courses in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Computer Vision (CV), Data Science (DS), Programming, and Databases especially SQL SERVER, MYSQL, ORACLE, and MS ACCESS for more than 5 years span. He has been working as a developer in the Microsoft Innovation Center and is now taking all that he has learned to help you discover amazing career opportunities. Please do not hesitate if you have any questions, I am always available for your help at any time to transform a passionate, enthusiastic learner into a skilled person.

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