• Insights
    • Webinars
    • Blog
    • Perspectives
    • Toolboxes
    • Events
    • Awards
  • Offering
    • Academy
      • Innovation courses
      • Innovation programs
    • Consulting
      • Onsite consultants
      • Project management
    • Services
      • Scout it
        • Business & strategy
        • Technology management
      • Invent it
        • Proposition management
        • Feasibility & IP development
      • Create it
        • Strategic design
        • Demonstrators & prototypes
      • Scale it
        • Go-to-market
        • Industrialization & production
    • Funding
      • Innovation grants
      • Seed capital
      • Subsidy applications
    • Solutions
      • MyStartUp
      • MyStartUp Portfolio
      • MyInnovationFactory
      • MyInnovationTalent
      • MyFutureProduct
  • Markets
    • Smart Space & Security
    • Smart FMCG
    • Smart Life Sciences
    • Smart Industry
  • Capabilities
    • Strategic Innovation
      • OpenLab
      • DesignLab
      • InnoLab
    • Digital innovation
      • AILab
      • DigitalLab
      • EmbeddedLab
    • Product innovation
      • MechLab
      • PhysicsLab
      • FabLab
    • High-tech innovation
      • OpticsLab
  • Technologies
    • Technology portfolio
    • IoT & sensors
    • AI & data science
    • Robotics & autonomy
    • Cooling, heating & fluidics
  • About
    • Our story
    • News
  • Jobs
  • Contact
Verhaert Masters in InnovationVerhaert Masters in Innovation
Verhaert Masters in InnovationVerhaert Masters in Innovation
  • Insights
        • Insights on current innovation and technology trends

        • Perspectives
        • Blog
        • Webinars
        • Toolboxes
        • Awards
  • Offerings
        • Services

        • Consulting

        • Solutions

        • Strategic innovation
        • Digital innovation
        • Product innovation
        • High-tech innovation
        • Design & engineering
        • Project & innovation management
        • MyStartUp - Disruptive innovation
        • MyInnovationFactory - Adjacent innovation
        • MyFutureProduct - Adjacent innovation
        • MyInnovationTalent - Core innovation
  • Markets
        • Smart Space & Security

        • Microgravity
          Earth observation
          Navigation
          Exploration
          Security
        • Smart FMCG

        • Dispensers
          Cooling & heating
          Servers
          Smart packaging
          Vending equipment
        • Smart Life Sciences

        • MedTech
          BioTech
          HealthTech
          Ophthalmic
        • Smart Industry

        • Mobility & logistics
          Chemical & material
          Home, building & construction
          Manufacturing & equipment
          Energy
  • Capabilities
        • Labs fueling integrated teams

        • OpenLab
        • DesignLab
        • InnoLab
        • DigitalLab
        • AILab
        • EmbeddedLab
        • MechLab
        • PhysicsLab
        • FabLab
        • OpticsLab
  • Technologies
        • Technologies

        • IoT & sensors
        • AI & data science
        • Robotics & autonomy
        • Cooling, heating & fluidics
        • Portfolio

  • About
        • About

        • News
        • Our story
  • Careers
  • Contact

The difference between Machine & Deep Learning

12 August 2020 Posted by Niels Verleysen Digital innovation, Perspectives

What’s the difference between Machine Learning & Deep Learning? And when to choose one or the other?

Banner - Perspective - The difference between Machine & Deep Learning


More classical Machine Learning techniques often require feature engineering. This means that someone uses their knowledge to list relevant features in the data. The upside is that you need less data because you utilize expert knowledge, the downside is the additional work and risk of no creating the right features. These features are then fed to the Machine Learning algorithm for training. The more features you select, the more computational power you need. If you don’t have unlimited power, consider the following:

  • Use as few features as possible.
  • Focus on minimum redundancy of features yet maximum relevance in the output (Correlation Based Feature Selection).
  • Good feature subsets contain features highly correlated with the classification, yet uncorrelated to each other (Correlation Based Feature Selection).

Visual - Difference machine and deep learning 1

Deep Learning is a more advanced technique within Machine Learning. It needs larger data sets and more computation power, but it offers automatic improvement and a high accuracy level.

In Deep Learning the task of finding relevant features is part of the algorithm and is automated. Deep Learning requires more data but less a priori knowledge and searches for the most optimal features, whereas with feature engineering we can not assume that the features are optimal.

In the example below, a Machine Learning algorithm detects a car from a picture. The algorithm learns from analyzing lots of car images marked as ‘car’ and other images marked as ‘not car’. Next, you can show other pictures to your algorithm and it will tell you if it’s a car or not. The example shows an additional feature extraction step for the feature-based “Machine Learning” that has to be performed by experts and programmers. The Deep Learning algorithm clearly incorporates that into its neural network.

Visual - Difference machine and deep learning 2

However, the algorithm won’t tell you how it came to its conclusion. You can test the algorithm to see if it consistently gives the right answers and after that you can decide to trust it or not. This process of validation is an important one and should be performed with care.

This is very similar to teaching a human to execute a task: you teach, test and give a diploma that says ‘this person has got this task covered’. You don’t analyze how their brain translates the question into the answer.

There are techniques that attempt to visualize how a Deep Learning algorithm interprets images in its internal workings, as shown below. It’s like trying to “read the mind of the neural network”. Visual - Visualization deep learning algorithm

Source: Medium.com


Download the perspective

Tags: Artificial intelligenceMachine & deep learning

You also might be interested in

Featured image - Integrating IoT in FMCG

Integrating IoT in FMCG

Jul 6, 2020

Leveraging the declining technology costs & power of AI, FMCG companies are increasingly developing IoT devices to boost customer loyalty.

The ultimate roadmap for self-learning products

The ultimate roadmap for self-learning products

Jun 13, 2022

Did you ever wonder how some intelligent products are incredibly good at certain tasks? Then you're at the right place. In this blog we're diving into the world of continuous learning.

Featured image - Webinar - Digital health services

Digital health services

Jun 8, 2020

Digital health services have advanced drastically and are changing healthcare ecosystems. How can using AI help you to join this trend?

OTHER CONTENT
  • Overcoming 15 bottlenecks in software development
  • AI-infused innovation
  • A new era in minimally-invasive spine surgery
NEWSLETTER



Verhaert Masters in Innovation is a pioneering innovation group helping companies and entrepreneurs to innovate, creating new products, businesses and services.

Verhaert icon LinkedIn Verhaert icon Facebook Verhaert icon SlideShare Verhaert icon YouTube Verhaert icon Twitter

SERVICES
FUNDING
SOLUTIONS
MARKETS
CAPABILITIES
TECHNOLOGY
PERSPECTIVES
BLOGS
WEBINARS
ABOUT
NEWS
JOBS
CONTACT

© 2023 Verhaert New Products & Services NV • BE 0439.039.420 • Privacy policy • Terms of use