Practical Deep Learning for Coders - Notes

Course Overview

• Course: Practical Deep Learning for Coders 2022, Part 1
• Location: Recorded at the University of Queensland
• Duration: 9 lessons, each ~90 minutes
• Target Audience: Individuals with coding experience wanting to apply deep learning and machine learning to practical problems.
• Free Access: No special hardware or software needed; free resources are provided.

Key Topics Covered

• Building and training deep learning models for:
  • Computer vision
  • Natural language processing (NLP)
  • Tabular analysis
  • Collaborative filtering
• Creating random forests and regression models
• Deploying models
• Using PyTorch, fastai, and Hugging Face libraries
• Educational Resources: Based on a 5-star rated book, available online for free

Course Benefits

• Real Results: Viewed over 6 million times, with numerous testimonials from alumni, academics, and industry experts.
• Notable Achievements:
  • Alumni have secured positions at top companies and published research at leading conferences.
  • Projects like dinosaur classifier and contributions to apps like Camera+.

Instructor

• Jeremy Howard:
  • Developer of fastai software
  • Extensive background in machine learning
  • Co-founder of fast.ai
  • Renowned for practical, example-driven teaching

Course Content Highlights

• Deep learning applications in various fields:
  • Natural language processing
  • Computer vision
  • Medical imaging and diagnostics
  • Biology and genomics
  • Image generation
  • Recommendation systems
  • Robotics and more
• Achieving state-of-the-art results in:
  • Image classification
  • Document classification
  • Tabular data analysis
  • Collaborative filtering
• Techniques and Concepts:
  • Random forests, gradient boosting
  • Affine functions, nonlinearities
  • Transfer learning, stochastic gradient descent
  • Data augmentation, weight decay
  • Entity and word embeddings

Tools and Software Used

• PyTorch: Foundation library for deep learning
• Fastai: High-level library built on PyTorch
• Hugging Face Transformers: For NLP tasks
• Gradio: For building interfaces and applications

Learning Approach

• Practical, example-first methodology
• Use of Jupyter Notebooks for interactive learning
• Platforms: Kaggle Notebooks, Paperspace Gradient
• Strong emphasis on community support and collaborative learning

Getting Started

• Detailed steps to access video lessons and materials
• Lesson Notes: Available for reference (e.g., lesson 7 and 8 notes)
• Platform Recommendation: Use cloud services for model training instead of local machines
• Community Support: Active forums and help available at forums.fast.ai

Conclusion

• The course is designed to make deep learning accessible to everyone with coding experience.
• Encompasses practical applications across diverse fields, enabling real-world problem-solving capabilities.