Course Details
Advanced Computer Vision: Deep Dive into CNNs, Object Detection, and Beyond
Data Science
Created On
July 01, 2023Description
The Advanced Computer Vision course explores complex concepts and applications in the field of computer vision, focusing on advanced algorithms and techniques. It equips students with advanced skills to analyze and understand visual data, solve challenging computer vision problems, and develop cutting-edge applications.
Overview
This comprehensive course is designed for individuals seeking an in-depth understanding of advanced computer vision techniques. It takes learners on a journey from the basics of Convolutional Neural Networks (CNNs) to cutting-edge object detection, image segmentation, and practical application development. Participants will explore a wide range of architectures, tools, and frameworks to equip themselves with the knowledge and skills required for advanced computer vision projects.
Features
- In-Depth CNN Understanding
- Practical Application
- Cutting-Edge Architectures: CNN architectures, including LeNet, AlexNet, VGGNet, Inception, and ResNet.
- Advanced Topics: Batch normalization, Transfer learning, and Hyperparameter tuning.
- Deep Dive into Object Segmentation
- Project-Based Learning:
- Portfolio Building
- Real-World Application
- Doubt Clearing session
- Email Support
- All 7 Days in a week Skype Support
What you'll learn
- Convolutional Neural Networks (CNNs)
- Advanced CNN Architectures
- Hyperparameter Tuning
- Data Augmentation
- Model Deployment
- Object Detection
- Object Tracking
- Web Application Development
- Image Segmentation
- GPU Utilization
Prerequisites
Curriculum
- 7 modules
Course Overview and Objectives
Expected Learning Outcomes
Tools Setup for the Course
Installing Anaconda, Pycharm, and Postman
Setting up and Managing Conda Environments
Introduction to Pycharm IDE
Integrating Pycharm with Conda
Integrating Pycharm with Virtual Environments (venv)-Integrating Pycharm with Pipenv
The Importance of CNNs
Building an Intuition for CNNs - CNN, Kernels, Channels, Feature Maps, Stride, Padding
Receptive Fields and Image Output Dimensionality Calculations
MNIST Dataset Explorations With CNN
MNIST CNN Intuition
Tensorspace.Js Visualization
CNN Explained
CIFAR 10 Dataset Explorations With CNN
Forward and Back Propagation
LeNet Architecture
AlexNet and VGGNet
Unconventional and Pure CNNs
Inception
InceptionV1 and Inception V2 Continued
Batch Normalization in CNNs
Inception
Introduction to Resnet Architecture
Resnet Architecture
Practical Implementation of Residual Networks for Image Classification
Utilizing OpenCV for Computer Vision Tasks
Introduction to Plant Disease Classification and Object Detection
TensorFlow Object Detection 1
TensorFlow Object Detection 2
Detectron 2 and Custom Training in Detectron 2
Pytorch Basics and FashionMNIST
Autograd in PyTorch
FashionMNIST Dataset and Transfer Learning
Object Classification and Deployment on Heroku
Object Classification and Deployment on AWS
GCP Deployment and Packaging
GPU Providers: AWS, GCP, Azure, Paperspace, DataCrunch, Floydhub
Introduction to YOLO (You Only Look Once)
YOLO Architecture: One-Stage Detection
YOLO Limitations and Trade-offs
SSD (Single Shot MultiBox Detector) for Efficient Object Detection
SSD Network Structure and Features
Mask RCNN Introduction
Mask RCNN Using TensorFlow
Annotation of Labelme and Conversion to TF Records
Mask RCNN for Image Segmentation
Implementing Mask R-CNN
Shredder Machine Project
RCNN (Region-based Convolutional Neural Network)
Face Recognition Techniques
Face Recognition Code Discussion
Fast RCNN
Fast RCNN Implementation
RPNN (Region Proposal Neural Network)
Project Discussion and Collaboration
Building a Detectron Web Application
Building a Detectron2 Web Application
Number Plate Detection Project
SSD (Single Shot MultiBox Detector) for Object Detection
Mask R-CNN: Advanced Image Segmentation
Instructors
Experienced Technostragist with three decades in IT, excelling in Sales, Product Management, and Marketing across IT hardware, networking, and software. Proven strategic planner, startup pioneer, and mentor for large teams. Marketing authority in areas such as Business Incubation, Branding, and Sales. Vast industry expertise spans ITES, IT Hardware, IT Training (Software), Distribution, and Retail. Recognized for interpersonal leadership, intuitive decision-making, and a collaborative approach. Complemented by four years as a Data Scientist, enhancing analytical and problem-solving skills with a deep passion for coding and a knack for simplifying complex concepts,
- 7 Modules
- 23 Hours
- Data Science
Login to Purchase the Course