Chapters
Transcript
Episode notes
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
Introduction
00:00 • 2min
Building and Shipping Features in a 30% Risky Environment
01:49 • 3min
The Future of Machine Learning in Elixir
04:20 • 2min
The Future of Machine Learning With Elixir
06:30 • 2min
Machine Learning in Elixir
08:55 • 3min
How to Deploy to Hugging Face in Your Web App
12:07 • 2min
How to Use Hugging Face to Improve Machine Learning
14:00 • 4min
How Elixir Works With Large Network Models
18:18 • 2min
How to Write a Neural Network in Python
20:20 • 2min
The Impact of Centos on the Open Source Community
22:42 • 4min
How to Launch a Live Book
26:43 • 3min
Live Book: A Code Notebook Platform for Machine Learning
29:24 • 3min
How to Use Smart Cells for an Elixir
32:09 • 4min
How to Build a Rich User Interface With Live Book
36:37 • 3min
How to Run Machine Learning Stuff in the Cloud
39:57 • 5min
How to Use Smart Cells to Improve Data Processing
44:47 • 3min
SQLite's Smart Cell for Busy Data
47:42 • 2min
The Future of Image Manipulation
49:50 • 2min
How Google XLA Decides Which Code to Run on the CPU or GPU
51:51 • 3min
TypeSense Cloud: A Fast Search as You Type Experience
54:24 • 4min
Elixir: A Distributed Machine Learning Notebook
58:37 • 3min
The Marketing Director of America's Elixir
01:01:51 • 3min
The Positive and Negative Sides of Working Together
01:04:59 • 3min
How to Make a Live Book
01:07:30 • 3min
The Importance of Reproducible Notebooks
01:10:54 • 4min
The Immutable Characteristics of Elixir
01:14:56 • 2min
PyAnNote: How to Make MP3 Transcripts Work for You
01:17:15 • 2min
How to Maintain a Month With GitHub
01:19:21 • 3min