Transfer Learning in modern DL: 3 AI Projects with PyTorch

Almost all cutting-edge AI applications use pretrained models rather than training from scratch, and transfer learning is one of the most useful techniques in contemporary deep learning.

In this course, you'll learn transfer learning from the ground up through clear theoretical explanations and three complete real-world projects.

You'll first build a strong conceptual understanding by learning:

1. What is Transfer Learning?

2. Knowledge Base and Knowledge Transfer

3. Source and Target Domains

4. Source and Target Tasks

5. Transfer Learning Workflow

6. Feature Extraction vs Fine-Tuning

7. Transfer Learning Terminologies

8. Types of Transfer Learning

9. Popular Pretrained Models architecture and applications:

   ResNet

   EfficientNet

   MobileNet

   Densenet

   VGGNet

   BERT

   ELMo

   Word2Vec

   Glove

   Whisper

   ASR

   text2speech

10. Advantages and Disadvantages of Transfer learning

Once you have mastered the theory, you will use these ideas in three real-world projects:

• Flower Image Prediction using MobileNet, ResNet50, and EfficientNetB0 with model comparison and fine-tuning.

• SaaS Ticket Routing using DistilBERT and TF-IDF Vectorization + Logistic Regression to categorize the customer complaints and compares performance with traditional machine learning approach and Transfer learning model.

• Video Caption Generation using faster Whisper for automatic speech-to-text transcription.

  
  

You will learn how to create, train, assess, compare, and implement transfer learning models while gaining practical experience with PyTorch throughout the course.

By the end of this course, you'll have both the theoretical knowledge and practical experience needed to confidently implement transfer learning in your own AI projects.