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Deep Learning on Azure with Python: Introduction to Deep Learning

Discover deep learning with Python using Microsoft Cognitive Toolkit, and explore deep learning algorithms and neural networks.

Deep Learning on Azure with Python: Introduction to Deep Learning
  • Duration4 weeks
  • Weekly study5 hours
  • 100% onlineTry this course for free
  • Included in an ExpertTrackCourse 4 of 5
  • Get full ExpertTrack access$39/month

In this hands-on introduction to deep learning, you will learn about different neural network types. You’ll develop your understanding of key deep learning vocabulary, concepts, and algorithm, enabling you to understand how deep learning frameworks work.

Deep learning is a highly advanced form of machine learning. At its core are deep learning neural networks, so-called because they are inspired by human learning and brain structures.

Unlike most machine learning, deep learning frameworks can process data from unstructured sources. Text analytics, and image and video processing allow deep learning frameworks to acquire information as we do.

Get practical experience of Python for deep learning

Deep learning algorithms can be used for a range of purposes, automating functions that once would have required human understanding. These include customer service, translation, and image analysis. Deep learning models can even write news stories. You’ll discover deep learning with Python programming. You will learn how to use Microsoft’s Cognitive Toolkit (CNTK) to build end-to-end neural networks, on Microsoft Azure’s cloud-based service.

Explore common frameworks for neural networks

You’ll build your skills and understanding in both the analysis and application of deep learning frameworks, including: multi-class Logistic Regression and MLP (Multi-Layered Perceptron) CNN (Convolution Neural Network) for text processing RNN (Recurrent Neural Network) to forecast time-series data LSTM (Long Short Term Memory) process sequential text data You’ll then move on to explore how to build end-to-end models using one or several of these neural networks to recognise hand-written digits.

What topics will you cover?

  • An end-to-end model for recognizing hand-written digit images using a multi-class Logistic Regression and MLP (Multi-Layered Perceptron).
  • CNN (Convolution Neural Network) model for improved digit recognition.
  • RNN (Recurrent Neural Network) model to forecast time-series data.
  • LSTM (Long Short Term Memory) model to process sequential text data.

What will you achieve?

By the end of the course, you‘ll be able to...

  • Explored the components of a deep neural network and how they work together
  • Explored the basic types of deep neural networks (MLP, CNN, RNN, LSTM) and the type of data each is designed for
  • Applied a working knowledge of vocabulary, concepts, and algorithms used in deep learning

Who is the course for?

This machine learning and artificial intelligence course is designed for those who would like to learn more about deep learning. Basic knowledge of python programming would be advantageous, as would solid maths and computer science skills.

Who developed the course?

CloudSwyft Global Systems, Inc.

CloudSwyft has partnered with the top global technology companies to deliver cutting edge digital skills learning across the modern workplace.

About this ExpertTrack

Discover deep learning in Azure in this ExpertTrack covering AI fundamentals, machine learning, and deep learning with Python.

Start learning today - free 7-day trial

After your free trial you can:

  • Pay $39 per month to keep learning online
  • Have complete control over your subscription; you can cancel any time
  • Work at your own pace and set your own deadlines at every stage
  • Only pay while you’re learning; the subscription will cancel automatically when you finish
  • Complete online assessments to test your knowledge and prove your skills
  • Earn digital course certificates and a final award that you can share online, with potential employers, and your professional network
  • Keep access to the content of courses you complete even after your subscription ends