AI and Machine Learning for Coders: A Programmer’s Guide to Artificial Intelligence by Laurence Moroney.
Laurence holds a BSc in Physics and Computer Science from Cardiff University, Postgraduate Diploma in Microelectronics Systems Design from Birmingham City University and a Graduate Certificate in Artificial Intelligence from Stanford University.
He was Global AI Advocacy Lead at Google for 10 years and Senior Developer Evangelist at Microsoft for 7 years. In fact, today he is Senior Fellow at The AI Fund, and Artificial Intelligence Advisor and Fellow at RealAvatar. Laurence has also taught millions of students through online platforms such as Coursera, Udacity, and edX.
This book is certainly an amazing guide designed for programmers looking to transition into AI and machine learning with a focus on computer vision, natural language processing (NLP), and sequence modeling with code samples and projects.
To no surprise Laurence is driving this on TensorFlow, Google’s tool for for building models on multiple platforms including Raspberry Pi for deployments on web, mobile (Android and iOS), cloud, and embedded systems.
Great learning with code examples
Laurence provides an introduction to AI and ML principles with
hands-on coding exercises. He also provides advice on data preparation, selecting the right model, training, and evaluation.
Consider this other book AI and Machine Learning for On-Device Development by Laurence which is focusing on driving AI and ML into the phone in your pocket:
Unlike many machine learning books that require a solid understanding of advanced mathematics, Laurence emphasizes practical lessons with code. The book certainly usable for beginners, and those with strong statistics and math backgrounds.
In conclusion, I found this to be another engaging book and code samples will really help readers transition to AI models under TensorFlow, Android, and iOS. Many readers should in fact, agree his examples are well designed and easy to follow, with line-by-line explanations of code. I was impressed with his creation of generative adversarial networks (GANs) with detailed examples.