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Deep Learning Explained

Deep Learning Explained

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9.2/10 (Our Score)
Product is rated as #17 in category Machine Learning

Machine learning uses computers to run predictive models that learn from existing data to forecast future behaviors, outcomes, and trends. Deep learning is a sub–field of machine learning, where models inspired by how our brain works are expressed mathematically, and the parameters defining the mathematical models, which can be in the order of few thousands to 100+ million, are learned automatically from the data. Deep learning is a key enabler of AI powered technologies being developed across the globe. In this deep learning course, you will learn an intuitive approach to building complex models that help machines solve real–world problems with human–like intelligence. The intuitive approaches will be translated into working code with practical problems and hands–on experience. You will learn how to build and derive insights from these models using Python Jupyter notebooks running on your local Windows or Linux machine, or on a virtual machine running on Azure. Alternatively, you can leverage the Microsoft Azure Notebooks platform for free. This course provides the level of detail needed to enable engineers / data scientists / technology managers to develop an intuitive understanding of the key concepts behind this game changing technology. At the same time, you will learn simple yet powerful …

Instructor Details

Steve is a big data geek and data scientist, with over two decades of experience using R and S/SPLUS for predictive analytics and machine learning. He holds a PhD degree in Geophysics from Princeton University, and has led multi-national data science teams across various companies

Specification: Deep Learning Explained

Duration

36 hours

Year

2020

Level

Intermediate

Certificate

Yes

Quizzes

No

1 review for Deep Learning Explained

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  1. Amol Nayak

    Very interesting and enjoyed taking the course. Good way to get started with CNTK. I will however not recommend using Azure notebooks for the assignments as its painfully slow especially for later assignments. Setup locally or use the Docker image on Mac as I did.

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