Recommended Books🧐🆕📚💻(update at 2024–07–01)

I will share a list of recommended books to enhance your skills in artificial intelligence and related fields (update at 2024–07–01).

Artificial Intelligence

Deep Learning at Scale: At the Intersection of Hardware, Software, and Data

Software Engineering for Data Scientists: From Notebooks to Scalable Systems

Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play

Time Series Forecasting in Python

Internet of Things Programming Projects: Build modern IoT solutions with the Raspberry Pi 3 and…

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable…

Practical Recommender Systems

Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems

Natural Language Processing in Action: Understanding, analyzing, and generating text with Python

Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep…

Real-World Natural Language Processing: Practical applications with deep learning

Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications

Effective Machine Learning Teams: Best Practices for Ml Practitioners

Natural Language Understanding with Python: Combine natural language technology, deep learning, and…

How to Solve It: A New Aspect of Mathematical Method

Introducing MLOps: How to Scale Machine Learning in the Enterprise

Deep Learning in Production

Deep Learning with Python, Second Edition

Software Architecture: The Hard Parts: Modern Trade-Off Analyses for Distributed Architectures

Probabilistic Machine Learning: Advanced Topics

AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence

TinyML Cookbook - Second Edition: Combine machine learning with microcontrollers to solve…

TinyML Cookbook: Combine artificial intelligence and ultra-low-power embedded devices to make the…

TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers

Hands-On Entity Resolution: A Practical Guide to Data Matching With Python

Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate…

Probabilistic Machine Learning: An Introduction

Machine Learning: A Probabilistic Perspective

Unifying Business, Data, and Code: Designing Data Products with JSON Schema

Fundamentals of Data Engineering: Plan and Build Robust Data Systems

Data Science from Scratch: First Principles with Python

Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications

Natural Language Processing with Transformers, Revised Edition

Automating Data Quality Monitoring: Scaling Beyond Rules with Machine Learning

Deciphering Data Architectures: Choosing Between a Modern Data Warehouse, Data Fabric, Data…

Amazon Redshift: The Definitive Guide: Jump-Start Analytics Using Cloud Data Warehousing

Fundamentals of Data Observability: Implement Trustworthy End-To-End Data Solutions

Data Quality Fundamentals: A Practitioner's Guide to Building Trustworthy Data Pipelines

Artificial Intelligence Basics: A Non-Technical Introduction

Multi-Objective Optimization Using Evolutionary Algorithms

Evolutionary Algorithms

Analyzing Evolutionary Algorithms: The Computer Science Perspective

Reinforcement Learning, second edition: An Introduction

Fluent Python: Clear, Concise, and Effective Programming

Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter

Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python

Deep Learning for Remote Sensing Images with Open Source Software

Practical Simulations for Machine Learning: Using Synthetic Data for AI

Synthetic Data for Deep Learning: Generate Synthetic Data for Decision Making and Applications with…

Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists

The Coming Wave: Technology, Power, and the Twenty-first Century's Greatest Dilemma

Understanding Machine Learning: From Theory to Algorithms

Feature Engineering and Selection: A Practical Approach for Predictive Models

The Art of Feature Engineering: Essentials for Machine Learning

Feature Engineering Bookcamp

Digital Image Processing and Analysis: Computer Vision and Image Analysis

Math and Architectures of Deep Learning

Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models…

Data Management at Scale: Modern Data Architecture with Data Mesh and Data Fabric

Architecting Data and Machine Learning Platforms: Enable Analytics and AI-Driven Innovation in the…

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

Pattern Recognition and Machine Learning

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques…

Mathematics for Machine Learning

Artificial Intelligence: A Modern Approach, Global Edition

Practitioner's Guide to Data Science

An Introduction to Statistical Learning: with Applications in Python

Deep Learning Patterns and Practices

Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play

Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model…

Springer Handbook of Computational Intelligence