Titlebook: Applied Deep Learning; Tools, Techniques, a Paul Fergus,Carl Chalmers Textbook 2022 Springer Nature Switzerland AG 2022 Deep Learning.Machi

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Deploying and Hosting Machine Learning Modelsimately, of course, after you have finished experimenting, you will need to consider a more production-friendly environment than your laptop. With the widespread industrial support and investment, this has been made easier through a variety of different frameworks. Tech giants such as Google, Facebo

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Enterprise Machine Learning Servingcan be used in a business pipeline. Access to these models can be direct or through model servers to support enterprise solutions. In the previous chapter, we also discussed how models can be accessed directly through library imports. In this chapter, we will discuss component-based MLOps and how mo

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https://doi.org/10.1007/978-3-476-04983-4 using symbolic AI to construct and interoperate language using syntax and semantic representations of language. Although these early attempts were impressive for the time, symbolic Natural Language Processing (NLP) failed to deliver anything near human-level abilities.

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2510-1765 ssible to everyone regardless of their experience.Provides a.This book focuses on the applied aspects of artificial intelligence using enterprise frameworks and technologies. The book is applied in nature and will equip the reader with the necessary skills and understanding for delivering enterprise

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https://doi.org/10.1007/978-3-642-93220-5ised learning models. This chapter will include data processing, feature engineering and model selection along with example algorithms. The two strands of supervised learning which includes classification and regression will also be discussed.