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
Last modified 4yr ago