Book Image

Solutions Architect’s Handbook - Second Edition

By : Saurabh Shrivastava, Neelanjali Srivastav
4 (2)
Book Image

Solutions Architect’s Handbook - Second Edition

4 (2)
By: Saurabh Shrivastava, Neelanjali Srivastav

Overview of this book

Becoming a solutions architect requires a hands-on approach, and this edition of the Solutions Architect's Handbook brings exactly that. This handbook will teach you how to create robust, scalable, and fault-tolerant solutions and next-generation architecture designs in a cloud environment. It will also help you build effective product strategies for your business and implement them from start to finish. This new edition features additional chapters on disruptive technologies, such as Internet of Things (IoT), quantum computing, data engineering, and machine learning. It also includes updated discussions on cloud-native architecture, blockchain data storage, and mainframe modernization with public cloud. The Solutions Architect's Handbook provides an understanding of solution architecture and how it fits into an agile enterprise environment. It will take you through the journey of solution architecture design by providing detailed knowledge of design pillars, advanced design patterns, anti-patterns, and the cloud-native aspects of modern software design. By the end of this handbook, you'll have learned the techniques needed to create efficient architecture designs that meet your business requirements.
Table of Contents (22 chapters)
20
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21
Index

Deep learning

ML is not just about forecasting numbers but also solving complex problems using neural language processing. These use cases include complex scenarios processed by the human brain, such as building an automated chatbot impersonating humans, reading handwritten text, image recognition, transcribing videos/audios, and converting text to audio and vice versa. Deep learning has the ability to solve such use cases by mimicking the human brain.

While ML needs a pre-defined set of labeled data using supervised learning, deep learning uses a neural network for unsupervised learning to simulate human brain behaviors by using a large amount of data to develop learning capabilities for machines. Deep learning is a neural network of multiple layers where you don't need to do data labeling upfront. However, you can use both labeled data and unlabeled data with deep learning, depending upon your use case. The following diagram shows a simple deep learning model:

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