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2D Deep Learning for Computer Vision

Learn State-of-the art Deep Learning Models for Computer Vision
Implement and Optimize models with Python, OpenCV, Keras, PyTorch.

In-depth knowledge with Quizzes, Assignments and Realtime Projects

2D Deep Learning for Computer Vision

Master Cutting-Edge AI Techniques for Image Analysis and Processing

Unlock the power of 2D deep learning to solve real-world computer vision challenges! This comprehensive course is designed for developers, engineers, and data scientists who want to build state-of-the-art AI models for image recognition, object detection, segmentation, and more.

Through a hands-on, project-driven approach, you’ll gain expertise in Convolutional Neural Networks (CNNs), advanced architectures, and deep learning optimization techniques—equipping you with the skills to develop high-performance computer vision applications.

✅ Core Concepts:
  • Fundamentals of deep learning in 2D computer vision
  • How CNNs work and why they dominate image processing
✅ Practical Skills:
  • Preprocess and augment image datasets for deep learning
  • Train, evaluate, and fine-tune CNN models for accuracy
  • Implement object detection, segmentation, and generative models
✅ Advanced Techniques:
  • Optimize networks for speed and efficiency
  • Deploy pretrained models (ResNet, VGG, YOLO, U-Net)
  • Use GANs and autoencoders for image synthesis
✅ Real-World Applications:
  • Medical imaging, autonomous vehicles, surveillance, and more


Course Highlights

📘 Modules: 12 well-structured modules

📚 Topics Covered: 140+ practical CV & AI topics

🛠️ Mini Applications: 15 real-world mini projects

🎥 Video Lectures: 45+ hours of high-quality instruction

🧪 Interactive Quizzes after each module

📝 Assignments to reinforce concepts

🚀 Project: Build a industry demanding AI project

🗂️ Curated Datasets for hands-on practice

🧑‍💻 Structured Jupyter Notebooks for every topic

🎯 Industry-Relevant Tools: Python, OpenCV, PyTorch, Keras, Albumentations…

💻 Source Code Access to all projects and mini-apps

📜 Certificate of Completion with project review

🙋 Doubt Clearing Support via forum/email (or live sessions)

🎓 1:1 Mentorship Option (on booking based)

🧠 Interview Preparation Tips at the end of the track

📊 Set of PPTs & 📄 Explainable Docs

📚 Course Modules Overview

  • ✅ Module 1: Introduction to Deep Learning in Computer Vision
  • ✅ Module 2: Foundations of 2D Deep Learning
  • ✅ Module 3: Image Preprocessing Techniques for Deep Learning
  • ✅ Module 4: Fundamentals of Convolutional Neural Networks (CNNs)
  • ✅ Module 5: CNN Architectures and Models in 2D Computer Vision
  • ✅ Module 6: Training and Evaluating CNN Models
  • ✅ Module 7: Advanced CNN Architectures and Techniques
  • ✅ Module 8: Object Detection and Localization
  • ✅ Module 9: Semantic Segmentation and Instance Segmentation
  • ✅ Module 10: Generative Models for Image Synthesis
  • ✅ Module 11: Feature Extraction and Representation Learning
  • ✅ Module 12: Network Optimization and Analysis

What You Will Learn

Master in-demand computer vision skills through hands-on projects and industry-relevant curriculum

🔍

Core Foundations

  • Fundamentals of 2D deep learning and neural networks
  • How CNNs revolutionized computer vision
  • Essential linear algebra for computer vision
🛠️

Tools & Libraries

  • Python for computer vision applications
  • OpenCV for image processing and feature extraction
  • PyTorch/Keras for building deep learning models
  • Scikit-learn for preprocessing and evaluation
💻

Practical Skills

  • Image preprocessing and augmentation techniques
  • Implementing CNN architectures from scratch
  • Transfer learning with pretrained models
  • Hyperparameter tuning for optimal performance
🚀

Advanced Techniques

  • Object detection with YOLO and Faster R-CNN
  • Semantic segmentation using U-Net architectures
  • Generative models (GANs, VAEs) for image synthesis
  • Model optimization and deployment strategies
🌎

Real-World Applications

  • Medical image analysis (X-ray, MRI segmentation)
  • Autonomous vehicle lane detection systems
  • Industrial quality inspection pipelines
  • Surveillance object tracking implementations
🎯

Career Preparation

  • Building a portfolio of computer vision projects
  • Solving real interview challenges from top companies
  • Understanding industry best practices and workflows
  • Staying current with emerging trends in CV/DL

Your Learning Journey:

📚

Conceptual Foundations through interactive lessons

👩💻

Hands-on Coding exercises with Jupyter notebooks

🏗️

Project-Based implementation of real-world systems

🔄

Continuous Evaluation through quizzes and assignments

🧰 Tools & Libraries You’ll Master in This Course

Python
NumPy
OpenCV
Scikit-Learn
Albumentations
Keras
PyTorch
TorchVision
Detectron2
ImageAI
MediaPipe
+ Latest AI Libraries

Learning Outcomes

By completing this course, you will:

🛠️

Master Essential Tools

  • Become proficient in Python, NumPy, OpenCV, Scikit-learn, Keras, and PyTorch
  • Implement complex deep learning pipelines from data preprocessing to deployment
  • Utilize OpenCV, PIL, and Scikit-learn for practical computer vision solutions
🔍

Apply Theory to Practice

  • Translate theoretical concepts into working implementations
  • Develop solutions for real-world problems using industry-standard tools
  • Bridge the gap between academic knowledge and production-ready code
🚀

Gain Project Experience

  • Build 10+ portfolio projects mirroring industry applications
  • Work with real datasets across multiple domains (medical, automotive, etc.)
  • Develop end-to-end computer vision systems
⚡

Master Optimization

  • Fine-tune CNN architectures for maximum performance
  • Apply quantization, pruning, and compression techniques
  • Optimize models for edge deployment and resource constraints
📈

Stay Industry-Relevant

  • Learn current best practices from computer vision practitioners
  • Implement state-of-the-art architectures (YOLOv8, Segment Anything, etc.)
  • Understand emerging trends in deep learning for CV
🎯

Job-Market Ready Skills

  • Solve real interview challenges from top tech companies
  • Develop production-grade code with proper documentation
  • Present technical solutions to both technical and non-technical audiences

🎉 Ultimate Outcome:

Graduate with practical experience in building computer vision systems using cutting-edge deep neural networks, ready to tackle real-world challenges in healthcare, autonomous vehicles, robotics, and more.

🧠 Hands-On Learning Journey

Master computer vision through practical challenges that mirror real-world scenarios

📝

Module Assignments

  • Weekly coding exercises reinforcing key concepts
  • Progressive difficulty from basic image processing to advanced CNNs
  • Automated grading with instant feedback
  • Example: “Implement edge detection using Sobel operators”
🧠

Knowledge Checks

  • Conceptual quizzes after each major topic
  • Timed challenges to test rapid problem-solving
  • Detailed explanations for all answers
  • Example: “Compare pooling strategies in CNNs”
🚀

Capstone Projects

  • End-to-end systems solving real business problems
  • Portfolio-ready with professional documentation
  • Industry datasets (medical, automotive, surveillance)
  • Examples:
    • Facial emotion recognition system
    • Self-driving car lane detection
    • Retail product recognition
💼

Interview Preparation

  • 100+ curated questions from top companies
  • Coding challenges with optimal solutions
  • System design for computer vision pipelines
  • Example: “How would you optimize a face detection model for mobile?”

📈 Progressive Skill Development

1

Master fundamentals through guided assignments

→
2

Validate understanding with concept quizzes

→
3

Build portfolio projects with real datasets

→
4

Prepare for interviews with company-specific drills

Sample Projects Overview

You’ll work on a series of real-world projects aligned with industry needs, applying concepts and tools you’ve mastered throughout the course.

Image Classification using CNNs

Build classifiers using CNNs like LeNet, VGG, or ResNet to categorize images (e.g., MNIST, CIFAR-10).

Object Detection & Localization

Apply YOLO, Faster R-CNN, and SSD to detect and localize multiple objects in real-time datasets.

Semantic Segmentation with U-Net

Use U-Net for pixel-wise segmentation in self-driving cars (e.g., road, pedestrians, vehicles).

Medical Image Segmentation

Segment MRI/CT scans using U-Net or DeepLab to identify organs, tumors, or anatomical zones.

GANs for Image Generation

Implement GANs like Pix2Pix or CycleGAN for image synthesis, transformation, and style transfer.

Style Transfer & Image Translation

Convert image domains (e.g., grayscale to color) and apply artistic style transformations.

Feature Visualization

Understand CNNs using techniques like activation maps, occlusion sensitivity, and saliency maps.

Transfer Learning

Use pre-trained CNNs (ResNet, Inception) for new image classification tasks and benchmark results.

Network Optimization

Experiment with dropout, batch normalization, and optimizers like Adam/SGD for model tuning.

Efficient CV Models for Edge

Apply model compression, pruning, and quantization to deploy fast, lightweight CV models.

🎓 Earn Your Certification – Showcase Your Expertise!

Upon successful completion of the course, you will receive a Certificate of Completion that includes your project titles and areas of expertise.

Imagine the impact! This official certificate is your ticket to proving your practical skills in Computer Vision. Seamlessly add it to your LinkedIn profile, professional resume, or online portfolio. It’s the perfect way to grab the attention of potential employers and confidently demonstrate your mastery in the field.

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