AI, ML and Data Engineering InfoQ Trends Report

1

Introduction

00:00 • 2min

2

Deep Learning Frameworks - Should You Start With Pytorch or Tenser Flow?

01:59 • 2min

3

How to Transfer Models Into Tenser Flo or Vicar?

03:43 • 2min

4

Is There a Framework for Distributed Training?

05:19 • 2min

5

Using Pre Trained Models for Deep Learning

06:50 • 2min

6

Deep Learning

08:33 • 2min

7

Are You Seeing Scalable Models on the Edge?

10:55 • 2min

8

Are You Seeing a Loss in Performance?

12:53 • 2min

9

Drontecnalt

14:32 • 2min

10

I Think the Next Frontier Is the Edge

16:19 • 2min

11

Robotics

18:25 • 2min

12

I Think the Next Challenge Is Scaling

19:59 • 2min

13

Kuda Programming Is the Way to Programm Gpus

21:50 • 2min

14

Writing Programs in Cuda Is a Good Metaphor for Quita Programming

23:39 • 2min

15

Is GPS Going to Make an Impact in the Self Driven Cars?

25:27 • 2min

16

Natural Language Processing and GPT Tree

27:37 • 2min

17

The Transformer Model Is Taking It a Lot of Applications Besides Just Inop

29:37 • 2min

18

The Benchmarks Are Not the Full Story, Right?

31:26 • 2min

19

Machine Learning Isn't Getting It Done Yet

33:07 • 2min

20

I've Written a Very Good Book, but It Ruins Poetry for Me

35:11 • 2min

21

Community - A Cloud Platform for Application Development

36:48 • 2min

22

Devops and Emelops - What Are the Moving Parts?

38:51 • 2min

23

Cann and Cubernitis - Two Dimensions of Machine Learning

41:03 • 2min

24

Devops vs DevOps - What's the Difference?

42:36 • 2min

25

M L Ops - Data Scientist or Data Engineer?

44:11 • 2min

26

Automating the Oral Process With Machine Learning

45:47 • 2min

27

Automated Machine Learning in a Tool Set

48:02 • 2min

28

Getting Started With Machine Learning

50:03 • 2min

29

Getting Started With Python Regression

51:39 • 2min

30

How to Train a Mobol Using Python

53:34 • 2min

31

Is There a Career in Mission Learning?

55:41 • 3min