Can you summarise each chapter of book design machine learning system by chip huyen
What a great book!

"Designing Machine Learning Systems" by Chip Huyen is a comprehensive guide to designing and building machine learning systems. Here's a summary of each chapter:

**Chapter 1: Introduction**

* Introduces the concept of machine learning system design and its importance
* Discusses the differences between machine learning model development and system design
* Outlines the goals and challenges of designing machine learning systems

**Chapter 2: Problem Formulation**

* Discusses the importance of problem formulation in machine learning system design
* Introduces the concept of problem definition, problem reframing, and problem decomposition
* Provides guidelines for formulating a machine learning problem, including identifying the problem type, defining the objective, and selecting evaluation metrics

**Chapter 3: Data Collection and Processing**

* Covers the importance of data in machine learning system design
* Discusses data collection strategies, including data sourcing, data scraping, and data generation
* Introduces data processing techniques, such as data cleaning, data transformation, and data augmentation

**Chapter 4: Data Splitting and Feature Engineering**

* Discusses the importance of data splitting in machine learning system design
* Introduces techniques for data splitting, including holdout method, k-fold cross-validation, and stratified splitting
* Covers feature engineering techniques, including feature selection, feature extraction, and feature creation

**Chapter 5: Model Selection and Training**

* Discusses the importance of model selection in machine learning
Chapter 1 introduces the book and discusses the motivation and overview of the book. Chapter 2 focuses on the basics of machine learning, including supervised and unsupervised learning, as well as the different types of models such as neural networks, support vector machines, and random forests. Chapter 3 dives into deep learning, covering convolutional neural networks, recurrent neural networks, and generative models. Chapter 4 discusses reinforcement learning, including Q-learning and policy iteration. Chapter 5 covers natural language processing, including topics such as text classification, sentiment analysis, and language translation. Chapter 6 introduces computer vision, including topics such as image classification, object detection, and segmentation. Finally, Chapter 7 provides an overview of the book and concludes the book.