Hyperparameter Optimisation Gives your ML Models Wings

Uploaded By: Myvideo

Published on

24 Jan 2022

9 views

0

0 votes

0

About Share Download Add to

Learn how to use hyperparameter optimisation to improve your machine learning models. Learn about the algorithms (grid search, random search, Bayesian optimisation) and the Python optimisation libraries available. Find out tips coming from the trenches that will save you time and hassle! ========================= Interested in hiring me as a consultant? Join The Sound Of AI Slack community: Connect with Valerio on Linkedin: Follow Valerio on Facebook: Follow Valerio on Twitter: ========================= 0:00 Intro 0:54 Parameters vs hyper-parameters 1:39 Hyper-parameter optimisation 2:24 Optimisation techniques 5:22 Build or re-use? 6:03 Optimisation libraries 6:49 Optimisation tips 7:44 What parameters s

Share with your friends

Link:

Embed:

<iframe width="640" height="360" src="//myvideo.cc/embed/eVFibTRaTHVHZkJjaUMzQVZLWkxtMFlXdVczMEN0NTVEWEpsek9MbU91Zz0" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>

Video Size:

Custom size:

x

Autoplay video

Hide player controls

Hide resume playing

Add to Playlist:

Favorites

My Playlist

Watch Later

Andrej Karpathy Let's reproduce GPT-2 (124M)

1 month ago

04:01:25

Andrej Karpathy Let's reproduce GPT-2 (124M)

15 77%

Let's reproduce GPT-2 (124M)

3 months ago

04:01:26

Let's reproduce GPT-2 (124M)

1 35%

TensorFlow in 100 Seconds

4 months ago

00:02:38

TensorFlow in 100 Seconds

2 40%

Python for Machine Learning full Course | Learn AI

11 months ago

08:12:37

Python for Machine Learning full Course | Learn AI

2 51%

Create a Large Language Model from Scratch with Python Tutorial

1 year ago

05:43:41

Create a Large Language Model from Scratch with Python Tutorial

47 91%

Is Simulating Wet Papers Possible

1 year ago

00:06:43

Is Simulating Wet Papers Possible

1 86%

RL + Model-based Control: Using On-demand Optimal Control to Learn Versatile Legged Locomotion

2 years ago

00:04:40

RL + Model-based Control: Using On-demand Optimal Control to Learn Versatile Legged Locomotion

1 43%

Science Note. Training ML models without hyper-parameter tuning - adaptive step-size procedures

2 years ago

00:36:53

Science Note. Training ML models without hyper-parameter tuning - adaptive step-size procedures

46 22%

Deep Learning Crash Course for Beginners

2 years ago

01:25:39

Deep Learning Crash Course for Beginners

7 59%

Bayesian Optimization in the Wild: Risk-Averse Decisions and Budget Constraints

2 years ago

00:56:38

Bayesian Optimization in the Wild: Risk-Averse Decisions and Budget Constraints

1 68%

100 Machine Learning tips and TRICKs to celebrate

2 years ago

00:26:06

100 Machine Learning tips and TRICKs to celebrate

8 60%

Deep Learning 2.0: How Bayesian Optimization May Power the Next Generation of DL by Frank Hutter

2 years ago

00:57:30

Deep Learning 2.0: How Bayesian Optimization May Power the Next Generation of DL by Frank Hutter

2 58%

udemy-hyperparameter-optimization-for-machine-learning-2021-5

3 years ago

09:24:24

udemy-hyperparameter-optimization-for-machine-learning-2021-5

10 12%

Designing New Energy Materials with Machine Learning with Rafael Gomez-Bombarelli - #558

3 years ago

00:55:09

Designing New Energy Materials with Machine Learning with Rafael Gomez-Bombarelli - #558

2 30%

Hyperparameter Optimisation Gives your ML Models Wings

3 years ago

00:10:04

Hyperparameter Optimisation Gives your ML Models Wings

9 19%

Introduction to Hyperparameters | Predictive Modeling and Machine Learning, Part 4

3 years ago

00:05:13

Introduction to Hyperparameters | Predictive Modeling and Machine Learning, Part 4

5 8%

Gaussian Processes

3 years ago

00:23:47

Gaussian Processes

13 26%

Intuitive & Scalable Hyperparameter Tuning with Apache Spark + Fugue

3 years ago

00:30:03

Intuitive & Scalable Hyperparameter Tuning with Apache Spark + Fugue

13 91%

GECCO2021 - tut121 - Introductory Tutorials - Evolution of Neural Networks

3 years ago

01:17:06

GECCO2021 - tut121 - Introductory Tutorials - Evolution of Neural Networks

4 12%

Determined AI + HuggingFace!

3 years ago

00:14:39

Determined AI + HuggingFace!

4 85%

PB2 - Population-Based Bandit Optimization

3 years ago

00:13:18

PB2 - Population-Based Bandit Optimization

1 32%

Directions in ML: Structured Models for Automated Machine Learning

3 years ago

01:02:01

Directions in ML: Structured Models for Automated Machine Learning

5 11%

Self-Tuning Networks: Amortizing the Hypergradient Computation for Hyperparameter Optimization

4 years ago

01:01:00

Self-Tuning Networks: Amortizing the Hypergradient Computation for Hyperparameter Optimization

10 62%

Recurrent Quantum Neural Networks, presented by Johannes Bausch, Cambridge University

4 years ago

01:15:46

Recurrent Quantum Neural Networks, presented by Johannes Bausch, Cambridge University

6 85%

0 Comments

Guest