Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Machine Learning Automation with TPOT

You're reading from   Machine Learning Automation with TPOT Build, validate, and deploy fully automated machine learning models with Python

Arrow left icon
Product type Paperback
Published in May 2021
Publisher Packt
ISBN-13 9781800567887
Length 270 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Dario Radečić Dario Radečić
Author Profile Icon Dario Radečić
Dario Radečić
Arrow right icon
View More author details
Toc

Table of Contents (14) Chapters Close

Preface 1. Section 1: Introducing Machine Learning and the Idea of Automation
2. Chapter 1: Machine Learning and the Idea of Automation FREE CHAPTER 3. Section 2: TPOT – Practical Classification and Regression
4. Chapter 2: Deep Dive into TPOT 5. Chapter 3: Exploring Regression with TPOT 6. Chapter 4: Exploring Classification with TPOT 7. Chapter 5: Parallel Training with TPOT and Dask 8. Section 3: Advanced Examples and Neural Networks in TPOT
9. Chapter 6: Getting Started with Deep Learning: Crash Course in Neural Networks 10. Chapter 7: Neural Network Classifier with TPOT 11. Chapter 8: TPOT Model Deployment 12. Chapter 9: Using the Deployed TPOT Model in Production 13. Other Books You May Enjoy

Overview of deep learning

Deep learning is a subfield of machine learning that focuses on neural networks. Neural networks aren't that new as a concept – they were introduced back in the 1940s but didn't gain much in popularity until they started winning data science competitions (somewhere around 2010).

Potentially the biggest year for deep learning and AI was 2016, all due to a single event. AlphaGo, a computer program that plays the board game Go, defeated the highest-ranking player in the world. Before this event, Go was considered to be a game that computers couldn't master, as there are so many potential board configurations.

As mentioned before, deep learning is based on neural networks. You can think of neural networks as directed acyclic graphs – a graph consisting of vertices (nodes) and edges (connections). The input layer (the first layer, on the far left side) takes in the raw data from your datasets, passes it through one or multiple...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image