Transfer Learning Explained 🤖 Step by Step Guide

Training a deep learning model from scratch takes huge datasets, long hours, and expensive compute power 💻. But what if you could reuse knowledge from models already trained on massive datasets? That’s the magic of Transfer Learning ✨.

In this video, we’ll walk through the 4 key steps of Transfer Learning, breaking it down for both beginners and professionals:

🔑 What you’ll learn in this video:

✅ Step 1: Select a Pre-trained Model (e.g., ResNet, VGG, BERT)

✅ Step 2: Freeze Layers to preserve learned features

✅ Step 3: Replace the Output Layer to fit your new task

✅ Step 4: Fine-tune the model with your dataset

💡 Key Benefits of Transfer Learning:

⚡ Reduced training time

📉 Less data required

📈 Improved model performance

🔄 Reuse existing knowledge from large-scale training

By the end of this video, you’ll understand how to leverage Transfer Learning to boost performance, save time, and achieve better results on your own ML tasks.

👉 Don’t forget to 👍 like, 🔔 subscribe, and 💬 share your questions in the comments — I’ll be happy to help!

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