Transfer Learning with PyTorch: Fine-Tune Vision & NLP

About this course

This course contains the use of artificial intelligence.

Stop training models from scratch. Transfer learning lets you take models that already learned from millions of examples and adapt them to your own problem, with a fraction of the data, time, and compute. This hands-on, intermediate-to-advanced course teaches transfer learning end to end in PyTorch, across both computer vision and natural language processing.

You'll start from the core ideas and a reusable PyTorch training workflow, then go deep on vision: feature extraction, fine-tuning with discriminative learning rates, modern backbones like ResNet, ConvNeXt and Vision Transformers, data and augmentation, and advanced topics including object detection, segmentation and CLIP. Then you'll master language transfer: pretrained transformers, fine-tuning BERT, token-level tasks, and parameter-efficient methods like LoRA and QLoRA that let you adapt huge models on a single GPU. Finally you'll evaluate honestly, export, optimize and serve your models, and build your own end-to-end project in the capstone.

The lessons are deliberately concise and high-density, and every concept is paired with a downloadable, runnable PyTorch lab. You'll write real code, beat real baselines, and finish with skills you can apply the next day.

What's inside:72 focused video lessons across 12 sections21 hands-on PyTorch labs with downloadable code8 quick-reference guidesA guided capstone project

By the end, you'll confidently choose a transfer strategy, fine-tune vision and language models, apply parameter-efficient methods, and deploy what you build.

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