Part 2 : Convolutional Neural Network | iNeuron

Welcome to Part 2 of our Convolutional Neural Network (CNN) series!

In this session we explore advanced concepts in CNNs and dive into practical applications, helping you to deepen your understanding and enhance your skills in deep learning and computer vision.

🔍 In this session, we will cover:

Recap of Basic CNN Concepts: Brief review of the foundational concepts covered in Part 1, including convolutional layers, activation functions, and pooling layers.

Transfer Learning: Understanding transfer learning and how to leverage pre-trained models to solve new tasks with limited data.

Fine-Tuning CNNs: Techniques for fine-tuning pre-trained models to improve performance on specific tasks.

-Regularization Techniques: Advanced regularization techniques to prevent overfitting, including:

Object Detection and Segmentation: Introduction to object detection and segmentation using CNNs, covering algorithms such as:

Practical Applications: Real-world applications of CNNs in various domains, such as:

Hands-On Implementation: Step-by-step guide to implementing advanced CNN concepts in Python using TensorFlow and PyTorch:

Setting up your Python environment
Preparing and preprocessing your dataset
Building and training the model
-Evaluating and fine-tuning the model

Project Showcase: Showcase of practical projects and case studies demonstrating the use of advanced CNN techniques.

Q&A and Troubleshooting: Addressing common issues and challenges faced when working with CNNs, and providing solutions and tips.
📚 Resources:


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