fast.ai

My personal notes from fast.ai course. These notes will continue to be updated and improved as I continue to review the course to "really" understand it.

• Deep Learning Part 1: 2018 Edition (v2): Oct - Dec 2017

• Deep Learning Part 1: 2019 Edition (v3): Oct - Dec 2018

• Deep Learning Part 2: 2017 Edition (v1): Feb - Apr 2017

• Deep Learning Part 2: 2018 Edition (v2): Mar - May 2018

• Machine Learning: 2017 Edition: Oct - Dec 2017

Main Course Links

• fastai library and Jupyter Notebook code

• Forum for part 1, v2

• Forum for part 1, v2 beginner

• Forum for part 2, v2 and alumni

• Forum for anything to do with deep learning that's not related to a fast.ai course

• Some fastai files, datasets and pre-trained models

• Forum for part 1, v3

• Forum for part 1, v3 advanced

Table of Contents

• Deep Learning Part 1: Practical Deep Learning for Coders

  • 2017 Edition (v1)

  • 2018 Edition (v2)

  • 2019 Edition (v3)

    • Lesson 1 - Image Recognition

    • Lesson 2 - Computer Vision: Deeper Applications

    • Lesson 3 - Multi-label, Segmentation, Image Regression, and More

    • Lesson 4 - NLP, Tabular, and Collaborative Filtering

    • Lesson 5 - Foundations of Neural Networks

    • Lesson 6 - Foundations of Convolutional Neural Networks

    • Lesson 7 - ResNets, U-Nets, GANs and RNNs

• Deep Learning Part 2: Cutting Edge Deep Learning for Coders

  • 2017 Edition (v1)

    • Lesson 8 - Artistic Style

    • Lesson 9 - Generative Models

    • Lesson 10 - Multi-modal & GANs

    • Lesson 11 - Memory Networks

    • Lesson 12 - Attentional Models

    • Lesson 13 - Neural Translation

    • Lesson 14 - Time Series & Segmentation

  • 2018 Edition (v2)

    • Lesson 8 - Object Detection

    • Lesson 9 - Single Shot Multibox Detector (SSD)

    • Lesson 10 - Transfer Learning for NLP and NLP Classification

    • Lesson 11 - Neural Translation; Multi-modal Learning

    • Lesson 12 - DarkNet; Generative Adversarial Networks (GANs)

    • Lesson 13 - Image Enhancement; Style Transfer; Data Ethics

    • Lesson 14 - Super Resolution; Image Segmentation with U-Net

• Machine Learning: Intro to Machine Learning for Coders

  • 2017 Edition

    • Lesson 1 - Introduction to Random Forests

    • Lesson 2 - Random Forest Deep Dive

    • Lesson 3 - Feature Engineering

    • Lesson 4 - Random Forest Interpretation

    • Lesson 5 - Train vs Test

    • Lesson 6 - What is Machine Learning and Why Do We Use It

    • Lesson 7 - Decision Trees Ensemble

    • Lesson 8 - Basic Neural Networks

    • Lesson 9 - SGD; Neural Network Training; Broadcasting

    • Lesson 10 - Logistic Regression; NLP; Naive Bayes

    • Lesson 11 - Structured and Time-Series Data

    • Lesson 12 - Entity Embeddings; Data Science and Ethics

Useful Resources

• fast.ai blog posts (by Jeremy Howard & Rachel Thomas)

• fast.ai FAQ for beginners

• fast.ai and deep learning key concepts

• Best practices

Tools for Deep Learning

• tmux

  • A terminal multiplexer

• Wget

  • A computer program that retrieves (downloads) content from web servers

• cURL

  • A command line tool for transferring data with URLs

• Secure Shell (SSH)

  • SSH client is used for securely connecting to remote computer

• SFTP (SSH File Transfer Protocol)

  • Securely transfer files between local computer and remote computer/server

• Jupyter Notebook

  • A web-based interactive computational environment for creating and sharing documents that contain live code, equations, visualizations and narrative text

• Kaggle API

  • Official Kaggle command line (CLI) tool to download Kaggle datasets