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Fastai's Flashcards

📕 Flashcards for the book Deep Learning for Coder with Fastai and PyTorch. 📕

⬇️ Download Decks

Download all decks or download the one you want:

  1. Your Deep Learning Journey
  2. From Model to Production
  3. Data Ethics
  4. Under the Hood: Training a Digit Classifier
  5. Image Classification
  6. Other Computer Vision Problems
  7. Training a State-of-the-Art Model
  8. Collaborative Filtering Deep Dive
  9. Tabular Modeling Deep Dive
  10. NLP Deep Dive: RNNs
  11. Data Munging with fastai's Mid-Level API
  12. A Language Model from Scratch
  13. Convolutional Neural Networks
  14. ResNets
  15. Application Architectures Deep Dive
  16. The Training Process
  17. A Neural Net from the Foundations
  18. CNN Interpretation with CAM
  19. A fastai Learner from Scratch

🚀 Get Started

💻 Desktop

  1. Download Anki
  2. Download all decks or one
  3. Import the deck:
    • Click on File
    • Click on Import...

📱 Mobile

  1. Download Anki (IOS/Android)
  2. Download all decks or one
  3. Import the deck

P.S.: If the desktop setup is already done, you could skip the steps #2 and #3 by synchronizing your decks with an Anki account.

🛠️ Contributing

If you want to contribute, review the contribution guidelines and the code of conduct.

Contributor Covenant

⭐ Acknowledgments

Fastai's contributors
  • Jeremy Howard
  • Sylvain Gugger
  • Joe Bender
  • SOVIETIC-BOSS88
  • alvarotap
  • Brad S
  • Jakub Duchniewicz
  • Jonathan Sum
  • holynec
  • Phillip Chu
  • Vijayabhaskar
  • lgvaz
  • pfcrowe
  • ricardocalleja
  • sirbots
  • Happy Sugar Life
  • Rubens
  • Steven Borg
  • Yikai Zhao
  • Ashwin Jayaprakash
  • Ben
  • Ben Mainye
  • Hamel Husain
  • Jeff Kriske
  • Jithendra Yenugula
  • Jordi Villar
  • Lee Yi Jie Joel
  • Minjeong
  • Niyas Mohammed
  • Tanner Gilbert
  • AKAMath
  • Abhinav Misra
  • Abhishek Sankar
  • Albert Villanova del Moral
  • Alexander Walther
  • Almog Baku
  • Alok
  • Amrit Purshotam
  • Anshul Joshi
  • Anthony DePasquale
  • Armin Berres
  • Austin Taylor
  • Benjamin van der Burgh
  • Cleon W
  • Daniel Strobusch
  • Daniel Wehner
  • Dien Hoa TRUONG
  • Eduard
  • Eric Daniels
  • Fabrizio Damicelli
  • Faisal Sharji
  • Gilbert Tanner
  • Giovanni Ruggiero
  • Gregory Bruss
  • Henry Webel
  • Hiromi Suenaga
  • Jacopo Repossi
  • Jakub Halmeš
  • Jared
  • Jimgao
  • Joel Mathew
  • Johannes Stutz
  • John Wu
  • Jophel Lyles
  • Jorge Avila
  • Josh Kraft
  • Kaito
  • Karel Ha
  • Kartikeya Bhardwaj
  • Kasim Te
  • Katrin Leinweber
  • Kerrick Staley
  • Kofi Asiedu Brempong
  • Leozítor Floro de Souza
  • Lloyd Jones
  • Luca Martial
  • Lucas Vazquez
  • Ludwig Schmidt-Hackenberg
  • Luke Smith
  • Maria Rodriguez
  • Matus-Dubrava
  • Michael Becker
  • Michelangelo Bucci
  • Mircea Ilie Ploscaru
  • MrFabulous
  • Musab
  • Nelson Chen
  • Nghia
  • Noè Rosanas
  • Pablo Wolter
  • Parul Pandey
  • Pedro Pereira
  • Pete Cooper
  • Petr Simecek
  • Prith
  • Priya Gautam
  • Rahim Nathwani
  • Rehman Amjad
  • Ritobrata Ghosh
  • Samuel El-Borai
  • Sarada Lee
  • Sarah
  • Sayantan Karmakar
  • Shaojun
  • Shin
  • Sirish
  • Sofyan Hadi Ahmad
  • Somnath Rakshit
  • TannerGilbert
  • Vineet Ahuja
  • Void01
  • Yurij Mikhalevich
  • akarri2001
  • alephthoughts
  • booletic
  • brett koonce
  • franperic
  • invictus2010
  • jeffreytjs
  • jhrun
  • maxfdama
  • miwojc
  • pakgembus
  • prairie-guy
  • seovalue
  • sgugger
  • tylerpoelking
  • unknown
  • 蔡舒起
  • 송석리(Song Sukree)
Tanishq Abraham's solutions
  1. Your Deep Learning Journey
  2. From Model to Production
  3. Data Ethics
  4. Under the Hood: Training a Digit Classifier
  5. Image Classification
  6. Other Computer Vision Problems
  7. Skipped
  8. Collaborative Filtering Deep Dive
  9. Tabular Modeling Deep Dive
  10. NLP Deep Dive: RNNs
  11. Data Munging with fastai's Mid-Level API
  12. A Language Model from Scratch
  13. Convolutional Neural Networks