Building Machine Learning and Deep Learning Models on Google Cloud PlatformJanuary 31, 2021
Building Machine Learning and Deep Learning Models on Google Cloud Platform
- Author: Ekaba Bisong
- ISBN: 1484244699
- Year: 2019
- Pages: 709
- Language: English
- File size: 31.3 MB
- File format: PDF, ePub
- Category: Cloud Computing
Just take a systematic approach to understand the fundamentals of machine learning and profound learning in the ground up and how they’re applied in practice. You will use this extensive guide for building and deploying learning models to deal with complex use cases while leveraging the computational tools of the Google Cloud Platform.
Writer Ekaba Bisong shows you how machine learning techniques and tools are used to predict or classify events based on a set of interactions between factors called attributes or attributes in a certain dataset. He teaches you how deep learning expands the machine learning algorithm of neural networks to learn complicated tasks that are hard for computers to perform, such as recognizing faces and understanding languages. And you will understand the way to leverage cloud calculating to quicken data science and machine learning deployments.
Building Machine Learning and Deep Learning Models on Google Cloud Platform is divided into eight parts that cover the essentials of machine learning and profound learning, the idea of information science and cloud solutions, programming for information science using the Python heap, Google Cloud Platform (GCP) infrastructure and products, advanced analytics on GCP, and deploying whole machine learning solution pipelines on GCP.
What You Will Learn
- Know the principles and fundamentals of machine learning and deep learning, the calculations, the Way to use them, when to utilize them, and how to interpret your results
- Know the programming concepts relevant to the machine and deep learning design and development utilizing the Python heap
- Build and translate machine and deep learning models
- Use Google Cloud Platform tools and services to develop and deploy large-scale machine learning and deep learning goods
- Be aware of the different facets and layout choices to consider when simulating a learning problem
- Productionalize machine learning units into applications products
Who This Book Is For
Beginners to the practice of data science and applied machine learning, data scientists in all levels, machine learning engineers, Google Cloud Platform information engineers/architects, and software