Modern Computer Vision & Deep Learning with Python & PyTorch

Modern Computer Vision & Deep Learning with Python & PyTorch, available at $54.99, has an average rating of 4.57, with 76 lectures, based on 100 reviews, and has 440 subscribers.

You will learn about Learn Computer Vision and Deep Learning with Real-world Applications in Python 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 Learn Deep Convolutional Neural Networks (CNN) for Computer Vision 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 This course is ideal for individuals who are 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 or 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 or Machine Learning Engineers, Deep Learning Engineers, and Data Scientists who want to apply Deep Learning to Computer Vision tasks or Developers, Graduates and Researchers who want to incorporate Computer Vision and Deep Learning capabilities into their projects or 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 It is particularly useful 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 or 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 or Machine Learning Engineers, Deep Learning Engineers, and Data Scientists who want to apply Deep Learning to Computer Vision tasks or Developers, Graduates and Researchers who want to incorporate Computer Vision and Deep Learning capabilities into their projects or 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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Summary

Title: Modern Computer Vision & Deep Learning with Python & PyTorch

Price: $54.99

Average Rating: 4.57

Number of Lectures: 76

Number of Published Lectures: 76

Number of Curriculum Items: 76

Number of Published Curriculum Objects: 76

Original Price: $79.99

Quality Status: approved

Status: Live

What You Will Learn

Learn Computer Vision and Deep Learning with Real-world Applications in Python
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
Learn Deep Convolutional Neural Networks (CNN) for Computer Vision
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 Should Attend

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

Target Audiences

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

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), 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
Graduatesand 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 Anyonewho 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!!

Course Curriculum

Chapter 1: Introduction to Course

Lecture 1: Introduction to Computer Vision Course

Chapter 2: What is Computer Vision & its Applications

Lecture 1: Introduction to Computer Vision and its Real-world Applications

Lecture 2: Major Computer Vision Tasks

Chapter 3: Deep Learning for Computer Vision

Lecture 1: Basics of Deep Learning for Computer Vision

Chapter 4: Computer Vision and Deep Convolutional Neural Networks

Lecture 1: Computer Vision using Convolutional Neural Networks (CNN)

Chapter 5: Setting-up Google Colab for Writing Python Code

Lecture 1: Introduction to Google Colab for Python Coding

Lecture 2: Connect Google Colab with Google Drive

Chapter 6: 1. Image Classification Task of Computer Vision

Lecture 1: Image Classification Task of Computer Vision with Pytoch and Python

Chapter 7: Pretrained Models for Single and Multi-Label Image Classification

Lecture 1: Introduction to Pretrained Models

Lecture 2: Deep Learning ResNet and AlexNet Architectures

Lecture 3: Access Data from Google Drive to Colab

Lecture 4: Data Preprocessing for Image Classification

Lecture 5: Single-Label Image Classification using ResNet and AlexNet PreTrained Models

Lecture 6: Single Label Classification Python and Pytorch Code

Lecture 7: Multi-Label Image Classification using Deep Learning Models

Lecture 8: Multi-Label Classification Python and PyTorch Code

Chapter 8: Transfer Learning for Image Classification

Lecture 1: Introduction to Transfer Learning

Lecture 2: Dataset, Data Augmentation, and Dataloaders

Lecture 3: Dataset for Classification

Lecture 4: FineTuning Deep ResNet Model

Lecture 5: HyperParameteres Optimization for Model

Lecture 6: Training Deep ResNet Model

Lecture 7: Fixed Feature Extractraction using ResNet

Lecture 8: Model Optimization, Training and Results Visualization

Lecture 9: Complete Python Code for Transfer Learning and Dataset

Chapter 9: 2. Semantic Segmentation Task Of Computer Vision

Lecture 1: Semantic Segmentation Task Of Computer Vision with Pytorch and Python

Lecture 2: Semantic Segmentation Real-World Applications

Chapter 10: Deep Learning Architectures For Segmentation (UNet, PSPNet, PAN)

Lecture 1: Pyramid Scene Parsing Network (PSPNet) For Segmentation

Lecture 2: UNet Architecture For Segmentation

Lecture 3: Pyramid Attention Network (PAN)

Lecture 4: Multi-Task Contextual Network (MTCNet)

Chapter 11: Segmentation Datasets, Annotations, Data Augmentation & Data Loading

Lecture 1: Datasets for Semantic Segmentation

Lecture 2: Data Annotations Tool for Semantic Segmentation

Lecture 3: Dataset for Semantic Segmentation

Lecture 4: Data Loading with PyTorch Customized Dataset Class

Lecture 5: Data Loading for Segmentation with Python and PyTorch Code

Lecture 6: Data Augmentation using Albumentations with Different Transformations

Lecture 7: Augmentation Python Code

Lecture 8: Learn To Implement Data Loaders In Pytorch

Chapter 12: Performance Metrics (IOU) For Segmentation Models Evaluation

Lecture 1: Performance Metrics (IOU, Pixel Accuracy, Precision, Recall, Fscore)

Lecture 2: Code (Python and PyTorch)

Chapter 13: Encoders and Decoders For Segmentation In PyTorch

Lecture 1: Transfer Learning And Pretrained Deep Resnet Architecture

Lecture 2: Encoders for Segmentation with PyTorch Liberary

Lecture 3: Decoders for Segmentation in PyTorch Liberary

Chapter 14: Implementation, Optimization and Training Of Segmentation Models

Lecture 1: Implement Segmentation Models (UNet, PSPNet, DeepLab, PAN, and UNet++)

Lecture 2: Segmentation Models Code with Python

Lecture 3: Learn To Optimize Hyperparameters For Segmentation Models

Lecture 4: Model Optimaztion Code (Python And PyTorch)

Lecture 5: Training of Segmentation Models

Lecture 6: Model Training Code (Python And PyTorch)

Chapter 15: Test Models and Visualize Segmentation Results

Lecture 1: Test Models and Calculate IOU,Pixel Accuracy,Fscore

Lecture 2: Test Models and Calculate Performance Scores (Python Code)

Lecture 3: Visualize Segmentation Results and Generate RGB Segmented Map

Lecture 4: Segmentation Results Visualization (Python Code)

Chapter 16: Complete Code and Dataset for Semantic Segmentation

Lecture 1: Final Code Review

Lecture 2: Complete Code and Dataset is Attached

Chapter 17: 3. Object Detection Task Of Computer Vision

Lecture 1: Object Detection Task Of Computer Vision with Python

Chapter 18: Deep Learning Architectures for Object Detection (R-CNN Family)

Lecture 1: Deep Convolutional Neural Network (VGG, ResNet, GoogleNet)

Lecture 2: RCNN Deep Learning Architectures for Object Detection

Lecture 3: Fast RCNN Deep Learning Architectures for Object Detection

Lecture 4: Faster RCNN Deep Learning Architectures for Object Detection

Lecture 5: Mask RCNN Deep Learning Architecture For Object Detection

Chapter 19: Detectron2 for Ojbect Detection

Lecture 1: Detectron2 for Ojbect Detection with PyTorch

Lecture 2: Perform Object Detection using Detectron2 Pretrained Models

Lecture 3: Python and PyTorch Code

Chapter 20: Training, Evaluating and Visualizing Object Detection on Custom Dataset

Lecture 1: Custom Dataset for Object Detection

Lecture 2: Dataset for Object Detection

Lecture 3: Train, Evaluate Object Detection Models & Visualizing Results on Custom Dataset

Lecture 4: Python and PyTorch Code

Chapter 21: Complete Code and Custom Dataset for Object Detection

Lecture 1: Resources: Code and Custom Dataset for Object Detection

Chapter 22: 4. Instance Segmentation Task of Computer Vision

Lecture 1: Instance Segmentation Task of Computer Vision with Python

Chapter 23: Mask RCNN for Instance Segmentation

Lecture 1: Mask RCNN for Instance Segmentation

Chapter 24: Training, Evaluating and Visualizing Instance Segmentation on Custom Dataset

Lecture 1: Custom Dataset for Instance Segmentation

Lecture 2: Train, Evaluate Instance Segmentation Model & Visualizing Results on Custom Data

Chapter 25: Complete Code and Custom Dataset for Instance Segmentation

Lecture 1: Resources: Complete Code and Custom Dataset for Instance Segmentation

Instructors

Dr. Mazhar Hussain
Deep Learning, Computer Vision, AI & Python | CS Lecturer
AI & Computer Science School
Learn AI, Deep Learning, & Computer Vision with Python

Rating Distribution

1 stars: 2 votes
2 stars: 3 votes
3 stars: 8 votes
4 stars: 11 votes
5 stars: 76 votes

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