Practical Deep Learning for Coders

Course snapshot

• Deep Learning
• Computer Vision
• Natural Language Processing
• Tabular Modeling
• Collaborative Filtering
• Transfer Learning
• Model Deployment
• PyTorch
• fastai
• Hugging Face
• Gradio
• SGD and Training Loops
• Data Augmentation
• Regularization (Weight Decay)

Practical Deep Learning is a free, project driven course from fast.ai that teaches you to build and deploy real models before diving into theory. Recorded at the University of Queensland, the program focuses on fast wins and progressive deepening. In the first two lessons you collect your own data, fine tune a modern vision model, and deploy it as a working app. From there you expand across modalities: computer vision, natural language processing, tabular data, and collaborative filtering. Each lesson layers concrete techniques like transfer learning, data augmentation, regularization, and practical training loop tweaks so you can systematically improve accuracy and robustness. The course uses PyTorch as the foundation, with the fastai library to speed up common workflows, plus Hugging Face and Gradio for modern NLP and quick app demos. You do not need special hardware or complex setup; the instructors show how to use cloud notebooks such as Kaggle and Paperspace with free tiers. Math is introduced only when needed so you get intuition first and detailed mechanics later. By the end you will have trained and deployed multiple models, understood why the key techniques work, and built the confidence to ship deep learning solutions to real problems.

Pricing and availability can change. Always check the provider page.

Deep learning is for everyone.— fast.ai

Syllabus

• Lesson 1: From models to results fast (vision classification, transfer learning)
  
• Lesson 2: Deploying your first model and collecting your own dataset
  
• Lesson 3: Data blocks, augmentation, and improving accuracy
  
• Lesson 4: NLP basics with transformers and embeddings
  
• Lesson 5: Tabular modeling and feature handling
  
• Lesson 6: Collaborative filtering and recommendation
  
• Lesson 7: Training loops, SGD, and regularization
  
• Lesson 8: Interpreting models, ethics, and responsible practice
  
• Lesson 9: Putting it all together and shipping
  

Instructors & Institution

Career outcomes

Admission & cost

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Last updated: 4 hours ago