Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners
內容描述
So you want to build learning models from the ground up, but find the rapidly changing world of machine learning and deep learning overwhelming and confusing, and you don't have a clue where to start. This book is your "one-stop shop" to understand the theoretical foundations and the practical steps to leverage machine learning and deep learning.
You will learn about machine learning tools and techniques used to predict or classify events based on a set of interactions between variables known as features or attributes in a particular dataset. And you will learn how deep learning extends machine learning algorithms of neural networks for learning complex tasks which are difficult for computers to perform such as recognizing faces and understanding languages. And you will know how the cloud is made up large sets of computers networked together in groups called data centers that are distributed across geographic locations and managed by companies such as Google, Microsoft, Amazon, and IBM and made available for public use by enterprises and personal users.
This book is a beginner's comprehensive guide for building learning models to address complex use cases using machine learning and deep learning principles and techniques while leveraging the computational resources and artificial intelligence (AI) capabilities of the Google Cloud Platform at a reasonable cost.
Building Machine Learning and Deep Learning Models on Google Cloud Platform is divided into six parts that cover the foundations of machine learning and deep learning, the concept of data science and cloud services, programming for data science and machine learning and deep learning using the Python stack, Google Cloud Platform infrastructure and products, and an end-to-end machine/deep learning project on the Google Cloud Platform.
What You'll Learn
Understand the principles and fundamentals of machine learning and deep learning, the algorithms, how to use them, when to use them, and how to interpret your results
Know the programming concepts relevant to machine and deep learning design and development using the Python stack
Build and interpret machine and deep learning models
Use Google Cloud Platform tools and services to develop and deploy machine learning and deep learning products
Be aware of the different facets and design choices to consider when modeling a learning problem
Productionalize machine learning models into software products
Who This Book Is For
Beginning software application developers. Experts in machine learning and deep learning design and modeling can benefit from this book as a refresher.
作者介紹
Ekaba Bisong is a data scientist at Pythian, a big data analytics company headquartered in Ottawa, Canada. He is also a master degree graduate student in the School of Computer Science at Carleton University with a research focus on learning systems (encompassing learning automata and reinforcement learning), machine learning, and deep learning. He is a Google Certified Professional Data Engineer. Teaching is his passion, and this book reflects his teaching philosophy of imparting knowledge in a way that incrementally takes the learner from the point of knowing nothing to the place where they can function as experts in the subject matter.
