Automated Machine Learning in Action

Optimize every stage of your machine learning pipelines with powerful automation components and cutting-edge tools like AutoKeras and KerasTuner.

In Automated Machine Learning in Action you will learn how to:
 

- Improve a machine learning model by automatically tuning its hyperparameters
- Pick the optimal components for creating and improving your pipelines
- Use AutoML toolkits such as AutoKeras and KerasTuner
- Design and implement search algorithms to find the best component for your ML task
- Accelerate the AutoML process with data-parallel, model pretraining, and other techniques

Automated Machine Learning in Action reveals how you can automate the burdensome elements of designing and tuning your machine learning systems. It’s written in a math-lite and accessible style, and filled with hands-on examples for applying AutoML techniques to every stage of a pipeline. AutoML can even be implemented by machine learning novices! If you’re new to ML, you’ll appreciate how the book primes you on machine learning basics. Experienced practitioners will love learning how automated tools like AutoKeras and KerasTuner can create pipelines that automatically select the best approach for your task, or tune any customized search space with user-defined hyperparameters, which removes the burden of manual tuning.

about the technology

Machine learning tasks like data pre-processing, feature selection, and model optimization can be time-consuming and highly technical. Automated machine learning, or AutoML, applies pre-built solutions to these chores, eliminating errors caused by manual processing. By accelerating and standardizing work throughout the ML pipeline, AutoML frees up valuable data scientist time and enables less experienced users to apply machine learning effectively.

about the book

Automated Machine Learning in Action shows you how to save time and get better results using AutoML. As you go, you’ll learn how each component of an ML pipeline can be automated with AutoKeras and KerasTuner. The book is packed with techniques for automating classification, regression, data augmentation, and more. The payoff: Your ML systems will be able to tune themselves with little manual work.

Product description

Review

“Automating automation itself is a new concept and this book does justice to it in terms of explaining the concepts, sharing real world advancements, use cases and research related to the topic. “ Satej KumarSahu

“A book with a lot of promise, covering a topic that's like to become hot in the next year or so. Read this now, and get ahead of the curve!” RichardVaughan

“A nice introduction to AutoML, its ambitions, and challenges bothin theory and in practice.” Alain Couniot

“Helps you to clearly understand the process of Machine Learning automation. The examples are clear, concise, and applicable to the real world.”Walter Alexander Mata López

“The author's friendly style makes novices feel ready to try outAutoML tools.” Gaurav Kumar Leekha

“A great book to take your machine learning skills to the next level.” Harsh Raval

“An impressive effort by the authors to break down a complex MLtopic into understandable chunks.” Venkatesh RajagopalTable of Contents
 

使用強大的自動化組件和先進的工具（如AutoKeras和KerasTuner），優化機器學習流程的每個階段。

在《自動化機器學習實踐》中，您將學習如何：
- 通過自動調整超參數來改進機器學習模型
- 選擇創建和改進流程的最佳組件
- 使用AutoML工具包，如AutoKeras和KerasTuner
- 設計和實施搜索算法，以找到最適合您的機器學習任務的組件
- 通過數據並行處理、模型預訓練和其他技術加速AutoML流程

《自動化機器學習實踐》揭示了如何自動化設計和調整機器學習系統中繁瑣的元素。本書以簡化的數學風格撰寫，並提供了豐富的實例，以應用AutoML技術到流程的每個階段。即使是機器學習新手也可以實施AutoML！如果您對機器學習不熟悉，您會喜歡本書對機器學習基礎知識的介紹。有經驗的從業者將喜歡學習如何使用AutoKeras和KerasTuner等自動化工具創建流程，這些工具可以自動選擇最佳方法來處理您的任務，或者調整任何自定義搜索空間的用戶定義超參數，從而減輕手動調整的負擔。

關於技術：
機器學習任務，如數據預處理、特徵選擇和模型優化，可能耗時且高度技術性。自動機器學習（AutoML）將預先構建的解決方案應用於這些繁瑣的任務，消除了手動處理引起的錯誤。通過加速和標準化整個機器學習流程的工作，AutoML釋放出寶貴的數據科學家時間，並使經驗較少的用戶能夠有效應用機器學習。

關於本書：
《自動化機器學習實踐》向您展示如何使用AutoML節省時間並獲得更好的結果。在閱讀過程中，您將學習如何使用AutoKeras和KerasTuner自動化機器學習流程的每個組件。本書充滿了自動化分類、回歸、數據增強等技術。回報是：您的機器學習系統將能夠以很少的手動工作自我調整。

產品描述：

評論：

“自動化自動化本身是一個新概念，本書在解釋概念、分享實際進展、用例和與該主題相關的研究方面做得很好。” - Satej KumarSahu

“這本書有很大的潛力，涵蓋了一個可能在未來一年左右變得熱門的主題。現在就閱讀它，超前一步！” - RichardVaughan

“這是一本很好的AutoML介紹書，介紹了它的野心和理論和實踐中的挑戰。” - Alain Couniot

“幫助您清楚地理解機器學習自動化的過程。例子清晰、簡潔，適用於現實世界。” - Walter Alexander Mata López

“作者友好的風格讓新手感覺準備好嘗試AutoML工具。” - Gaurav Kumar Leekha

“一本將您的機器學習技能提升到更高水平的好書。” - Harsh Raval

“作者們將一個複雜的機器學習主題分解成易於理解的部分，這是一個令人印象深刻的努力。” - Venkatesh Rajagopal

目錄：

Qingquan Song, Haifeng Jin, and Dr. Xia "Ben" Hu are the creators of the AutoKeras automated deep learning library. Qingquan and Haifeng are PhD students at Texas A&M University, and have both published papers at major data mining conferences and journals. Dr. Hu is an associate professor at Texas A&M University in the Department of Computer Science and Engineering, whose work has been utilized by TensorFlow, Apple, and Bing.

Qingquan Song、Haifeng Jin和Dr. Xia "Ben" Hu是AutoKeras自動化深度學習庫的創作者。Qingquan和Haifeng是德克薩斯A＆M大學的博士生，並在主要的數據挖掘會議和期刊上發表了論文。Dr. Hu是德克薩斯A＆M大學計算機科學和工程系的副教授，他的工作被TensorFlow、Apple和Bing所使用。

Table of Contents
PART 1 FUNDAMENTALS OF AUTOML
1 From machine learning to automated machine learning
2 The end-to-end pipeline of an ML project
3 Deep learning in a nutshell
PART 2 AUTOML IN PRACTICE
4 Automated generation of end-to-end ML solutions
5 Customizing the search space by creating AutoML pipelines
6 AutoML with a fully customized search space
PART 3 ADVANCED TOPICS IN AUTOML
7 Customizing the search method of AutoML
8 Scaling up AutoML
9 Wrapping up

目錄

第一部分 自動機器學習基礎

1 從機器學習到自動機器學習

2 機器學習專案的端到端流程

3 簡介深度學習

第二部分 實踐中的自動機器學習

4 自動生成端到端機器學習解決方案

5 通過創建自動機器學習流程自定義搜索空間

6 具有完全自定義搜索空間的自動機器學習

第三部分 自動機器學習的高級主題

7 自定義自動機器學習的搜索方法

8 擴展自動機器學習

9 總結