Software Engineering Radio - the podcast for professional software developers Episode 391: Jeremy Howard on Deep Learning and fast.ai
26 snips
Dec 5, 2019 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
Introduction
00:00 • 6min
Deep Learning - What Other Problems Can You Solve With It?
05:42 • 2min
A Deep Learning Model - Is There a Label for That?
07:30 • 2min
Train a Model to Recognize Cats and Dogs From Scratch
09:21 • 3min
Using Pre Trained Networks in Deep Learning
11:59 • 2min
Machine Learning - What Are the Predictors of a Successful Career?
14:20 • 2min
Deep Learning - What You Need to Know
16:20 • 3min
How to Build a High Level Deep Learning Course in Python
19:46 • 2min
Train Your Own Models
21:19 • 2min
Using Pretrained Models and Transfer Learning in Text Analysis
23:10 • 5min
Are the Current Weights Good or Bad?
28:33 • 3min
Using the Fastda Library to Debug and Test a Model
31:23 • 3min
What Is a Jupiter Note Book?
34:12 • 3min
Learning Rate - How to Find the Right Learning Rate
37:17 • 3min
Is Your Model Learning the Fastest at the Rate of Learning?
40:12 • 2min
The Universal Approximation Theorem
42:38 • 2min
How to Implement a Deep Learning System From Scratch
44:48 • 3min
The Importance of Swift in Deep Learning
47:39 • 3min
How to Setup a PyTorch Model
50:52 • 2min
Do You Need a Gpu for Multi Jeep Training?
53:17 • 2min
Jeremy Howard on Soft Engineering Radio
54:47 • 2min