Practical Deep Learning for Coders - Lesson 1

Welcome and Introduction

• Version five of the course
• The first new update in two years
• Major advancements in deep learning since 2015

XKCD Comic Reference

• XKCD comic from 2015 about bird photo recognition
• In 2015, recognizing bird photos was seen as nearly impossible
• Today, it can be done easily using deep learning

Building an Image Recognition System

Steps to Create the “Is it a bird?” System

1. Search for Images
   • Using Python to search for bird images on DuckDuckGo
   • Download and resize images to 400 pixels for optimization
2. Data Cleaning
   • Verify and remove broken images
3. Creating a Data Block
   • Define input and output for the computer vision model
   • Visual validation of images (bird and forest)
4. Model Training
   • Train the model using the downloaded images
   • Running the model on a laptop without the need of vast data centers
5. Model Evaluation
   • Use the model to predict if an image is a bird
   • Achieved nearly perfect accuracy

Recent Advances in Deep Learning

• DALL·E·2 for generating images from text prompts
• Example outputs from inputting Twitter bios and other text prompts

Practical and Ethical Considerations

• Ethical implications of deep learning advancements
• Access the full data ethics course at ethics.fast.ai

Importance of Student Feedback

• Innovative colored cup system for real-time feedback
• Online version for remote learners at cups.fast.ai/fast

Introduction to the Educators and Course Structure

• Jeremy Howard, course instructor
• Educational research informing teaching methods
• Focus on practical applications rather than theoretical foundations

Key Learning Points for the Course

• Build and deploy deep learning models quickly and effectively
• Start with practical model training before diving into theoretical depth
• Use Python and Jupyter Notebooks for hands-on learning
• Fast.ai library simplifies many deep learning tasks

Example of Building and Using a Model in Jupyter Notebooks

Running Code in Jupyter Notebooks

• Use of Jupyter notebooks for interactive learning
• Example of downloading and analyzing bird images
• Training a model using fast.ai's data block API

Key Commands and Functions

• download_images(), resize_images(), show_batch()
• Fast.ai's fine_tune() method for transfer learning

Other Types of Models and Applications

1. Segmentation Models
   • Classifying each pixel in an image
   • Very quick training with minimal data
2. Tabular Models
   • Analyzing spreadsheet and database tables
   • Example: predicting salaries based on demographic data
3. Collaborative Filtering Models
   • Basis of recommendation systems (e.g., Spotify, Apple Music)
   • Predict user preferences based on past behavior

Notebooks for Presentations and Documentation

• Use of Rise for turning Jupyter notebooks into presentations
• Example usage in writing books, testing software, and blogging

Current and Future Applications of Deep Learning

• Deep learning in NLP, computer vision, medicine, etc.
• Continuous advancements and new applications being explored

Foundational Concepts in Machine Learning and Neural Networks

• Basic idea: replace traditional programs with models trained on data
• Process: initialize weights, compute loss, update weights iteratively

Course Assignments and Community Participation

• Experimentation and hands-on practice encouraged
• Share work on the course forum for feedback and community support
• Practical projects and exercises to apply learning

Summary and Next Steps

• Read Chapter 1 of the course book
• Complete exercises and quizzes
• Participate actively in the course forum