04:26

Reducing Loss

01:43

Regularization for Sparsity

04:20

Regularization for Simplicity

01:47

Framing [id]

02:52

[id] Descending into ML

14:36

[id] embeddings

07:42

[id] classification

04:05

[id] data dependencies

04:41

[id] reg for simplicity

01:29

[id] course summary

02:54

ml intro norvig (replacement)

01:17

Every Engineer is also a Writer

10:51

Technical Writing Two Facilitators: Teaching Exercise 4

01:52

Technical Writing Two Facilitators: Teaching Intermezzo 2

03:46

Technical Writing Two Facilitators: Teaching Exercise 3

02:26

Technical Writing Two Facilitators: Teaching Intermezzo 1

03:59

Technical Writing Two Facilitators: Teaching Exercise 2

02:38

Technical Writing Two Facilitators: Teaching Exercise 1

02:17

Technical Writing Two Facilitators: Course Introduction

01:51

Technical Writing One Facilitators: Teaching Exercise 6

03:29

Technical Writing One Facilitators: Teaching Exercise 5

04:16

Technical Writing One Facilitators: Teaching Exercise 4

02:11

Technical Writing One Facilitators: Teaching the Intermezzo

04:04

Technical Writing One Facilitators: Teaching Exercise 3

03:27

Technical Writing One Facilitators: Teaching Exercise 2

02:27

Technical Writing One Facilitators: Teaching Exercise 1

02:18

Technical Writing One Facilitators: Logistics

02:10

Technical Writing One Facilitators: Course Introduction

00:42

Active Voice vs Passive Voice

00:49

Audience Match

10:48

Rules of ML

03:43

Multi-Class Neural Nets

02:43

Literature Example

02:02

Cancer Example

04:02

Data Dependencies

02:16

Static vs. Dynamic Inference

02:18

Static vs. Dynamic Training

01:04

Production ML Systems

14:44

Embeddings

02:53

Training Neural Nets

02:50

Intro to Neural Nets

01:42

Regularization for Sparsity

07:26

Classification

03:41

Logistic Regression

04:20

Regularization for Simplicity

04:04

Feature Crosses

05:43

Representation

01:46

Validation

01:46

Training and Test Sets

04:48

Generalization

00:38

First Steps with TensorFlow

04:23

Reducing Loss

02:54

Descending into ML

00:56

Course Overview

03:13

Machine Learning Practicum: Image Classification

02:54

Introducing ML