Modern Computer Vision & Deep Learning With Python & Pytorch

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

Requirements

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

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 applicationsLearn Deep Convolutional Neural Networks (CNN) for Computer VisionYou 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 SegmentationData Augmentation and Data Loading in PyTorch for Semantic SegmentationPerformance Metrics (IOU) for Segmentation Models EvaluationTransfer Learning and Pretrained Deep Resnet ArchitectureSegmentation Models Implementation in PyTorch using different Encoder and Decoder ArchitecturesHyperparameters Optimization and Training of Segmentation ModelsTest Segmentation Model and Calculate IOU, Class-wise IOU, Pixel Accuracy, Precision, Recall and F-scoreVisualize Segmentation Results and Generate RGB Predicted Segmentation MapLearn Object Detection using Deep Learning Models with PytorchLearn RCNN, Fast RCNN, Faster RCNN and Mask RCNN ArchitecturesPerform Object Detection with Fast RCNN and Faster RCNNIntroduction to Detectron2 by Facebook AI Research (FAIR)Preform Object Detection with Detectron2 ModelsExplore Custom Object Detection Dataset with AnnotationsPerform Object Detection on Custom Dataset using Deep LearningTrain, Test, Evaluate Your Own Object Detection Models and Visualize ResultsPerform Instance Segmentation using Mask RCNN on Custom Dataset with Pytorch and PythonWho 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 problemsMachine 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 projectsGraduates and Researchers in Computer Science, Electrical Engineering, and other related fields who want to learn about the latest advances in Deep Learning for Computer VisionIn 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 PyTorchThis 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!!

Overview

Section 1: Introduction

Lecture 1 Introduction to Course

Section 2: What is Computer Vision & its Applications

Lecture 2 Introduction to Computer Vision and its Real-world Applications

Lecture 3 Major Computer Vision Tasks

Section 3: Deep Convolutional Neural Networks (CNN) for Computer Vision

Lecture 4 Introduction to Convolutional Neural Networks (CNN)

Section 4: Setting-up Google Colab for Writing Python Code

Lecture 5 Introduction to Google Colab for Python Coding

Lecture 6 Connect Google Colab with Google Drive

Section 5: Image Classification Task of Computer Vision

Lecture 7 Introduction to Single and Multi-label Image Classification

Section 6: Pretrained Models for Single and Multi-Label Image Classification

Lecture 8 Introduction to Pretrained Models

Lecture 9 Deep Learning ResNet and AlexNet Architectures

Lecture 10 Access Data from Google Drive to Colab

Lecture 11 Data Preprocessing for Image Classification

Lecture 12 Single-Label Image Classification using ResNet and AlexNet PreTrained Models

Lecture 13 Single Label Classification Python and Pytorch Code

Lecture 14 Multi-Label Image Classification using Deep Learning Models

Lecture 15 Multi-Label Classification Python and PyTorch Code

Section 7: Transfer Learning for Image Classification

Lecture 16 Introduction to Transfer Learning

Lecture 17 Dataset, Data Augmentation, and Dataloaders

Lecture 18 Dataset for Classification

Lecture 19 FineTuning Deep ResNet Model

Lecture 20 HyperParameteres Optimization for Model

Lecture 21 Training Deep ResNet Model

Lecture 22 Fixed Feature Extractraction using ResNet

Lecture 23 Model Optimization, Training and Results Visualization

Lecture 24 Complete Python Code for Transfer Learning and Dataset

Section 8: Semantic Segmentation Task Of Computer Vision

Lecture 25 Introduction to Semantic Image Segmentation

Lecture 26 Semantic Segmentation Real-World Applications

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

Lecture 27 Pyramid Scene Parsing Network (PSPNet) For Segmentation

Lecture 28 UNet Architecture For Segmentation

Lecture 29 Pyramid Attention Network (PAN)

Lecture 30 Multi-Task Contextual Network (MTCNet)

Section 10: Segmentation Datasets, Annotations, Data Augmentation & Data Loading

Lecture 31 Datasets for Semantic Segmentation

Lecture 32 Data Annotations Tool for Semantic Segmentation

Lecture 33 Data Loading with PyTorch Customized Dataset Class

Lecture 34 Data Loading for Segmentation with Python and PyTorch Code

Lecture 35 Data Augmentation using Albumentations with Different Transformations

Lecture 36 Augmentation Python Code

Lecture 37 Learn To Implement Data Loaders In Pytorch

Section 11: Performance Metrics (IOU) For Segmentation Models Evaluation

Lecture 38 Performance Metrics (IOU, Pixel Accuracy, Precision, Recall, Fscore)

Lecture 39 Code (Python and PyTorch)

Section 12: Encoders and Decoders For Segmentation In PyTorch

Lecture 40 Transfer Learning And Pretrained Deep Resnet Architecture

Lecture 41 Encoders for Segmentation with PyTorch Liberary

Lecture 42 Decoders for Segmentation in PyTorch Liberary

Section 13: Implementation, Optimization and Training Of Segmentation Models

Lecture 43 Implement Segmentation Models (UNet, PSPNet, DeepLab, PAN, and UNet++)

Lecture 44 Segmentation Models Code with Python

Lecture 45 Learn To Optimize Hyperparameters For Segmentation Models

Lecture 46 Model Optimaztion Code (Python And PyTorch)

Lecture 47 Training of Segmentation Models

Lecture 48 Model Training Code (Python And PyTorch)

Section 14: Test Models and Visualize Segmentation Results

Lecture 49 Test Models and Calculate IOU,Pixel Accuracy,Fscore

Lecture 50 Test Models and Calculate Performance Scores (Python Code)

Lecture 51 Visualize Segmentation Results and Generate RGB Segmented Map

Lecture 52 Segmentation Results Visualization (Python Code)

Section 15: Complete Code and Dataset for Semantic Segmentation

Lecture 53 Final Code Review

Lecture 54 Complete Code and Dataset is Attached

Section 16: Object Detection Task Of Computer Vision

Lecture 55 Object Detection and its Applications

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

Lecture 56 Deep Convolutional Neural Network (VGG, ResNet, GoogleNet)

Lecture 57 RCNN Deep Learning Architectures for Object Detection

Lecture 58 Fast RCNN Deep Learning Architectures for Object Detection

Lecture 59 Faster RCNN Deep Learning Architectures for Object Detection

Section 18: Detectron2 for Ojbect Detection

Lecture 60 Detectron2 for Ojbect Detection with PyTorch

Lecture 61 Perform Object Detection using Detectron2 Pretrained Models

Lecture 62 Python and PyTorch Code

Section 19: Training, Evaluating and Visualizing Object Detection on Custom Dataset

Lecture 63 Custom Dataset for Object Detection

Lecture 64 Dataset for Object Detection

Lecture 65 Train, Evaluate Object Detection Models & Visualizing Results on Custom Dataset

Lecture 66 Python and PyTorch Code

Section 20: Complete Code and Custom Dataset for Object Detection

Lecture 67 Resources: Code and Custom Dataset for Object Detection

Section 21: Instance Segmentation Task of Computer Vision

Lecture 68 What is Instance Segmentation

Section 22: Mask RCNN for Instance Segmentation

Lecture 69 Mask RCNN for Instance Segmentation

Section 23: Training, Evaluating and Visualizing Instance Segmentation on Custom Dataset

Lecture 70 Train, Evaluate Instance Segmentation Model & Visualizing Results on Custom Data

Section 24: Complete Code and Custom Dataset for Instance Segmentation

Lecture 71 Resources: Complete Code and Custom Dataset for Instance Segmentation

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