All About Deep Learning

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Deep learning is a machine learning technique that allows computers to learn by example, the same way that humans do. Deep learning is a critical component of self-driving automobiles, allowing them to detect a stop sign or differentiate between a pedestrian and a lamppost. It enables voice control in consumer electronics such as phones, tablets, televisions, and hands-free speakers.

Deep learning has gotten a lot of press attention recently, and with good cause. It’s accomplishing results that were previously unattainable. Deep learning technologies are a primary focus for internet giants like Google and Facebook, as well as anyone who wants to make use of data, with hardware speeds hitting new heights and more data being generated than ever before.

Slide by Andrew Ng source:

How Deep Learning Works

Results Get Better With More Data, Larger Models, More Compute. Slide by Jeff Dean, All Rights Reserved

Deep learning models are sometimes referred to as deep neural networks since most deep learning approaches

use a neural network (an adaptive system that learns by connecting interconnected nodes or neurons in a layered structure that resembles a human brain).

The number of hidden layers in a neural network is commonly referred to as “deep.” Deep neural networks can have up to 150 hidden layers, whereas traditional neural networks only have 2-3.

Large sets of labeled data and neural network architectures that learn features directly from the data without the requirement for manual feature extraction are used to train deep learning models.

Neural networks are organized in layers consisting of a set of interconnected nodes. Networks can have tens or hundreds of hidden layers.

Convolutional neural networks (CNN or ConvNet) are one of the most popular types of deep neural networks. A CNN uses 2D convolutional layers to combine learned features with input data, making it well suited to processing 2D data like photos.

Deep Learning Applications

Deep learning algorithms are used in a variety of industries:

Automated Driving

Deep learning is being used by automotive researchers to detect items such as stop signs and traffic lights automatically. Furthermore, deep learning is used to recognize pedestrians, which aids in the reduction of accidents.

Aerospace and Defense

Deep learning is used in aerospace and defense to recognize items from satellites that pinpoint areas of interest, as well as to indicate safe and risky zones for personnel.

Medical Research

Cancer researchers detect cancer cells automatically. UCLA researchers developed a high-dimensional data set that was used to train a deep learning application to reliably identify cancer cells.

Industrial Automation

Deep learning is assisting in the improvement of worker safety around heavy machinery by automatically recognizing whether persons or objects are within a dangerous distance of the machines.


Deep learning is being utilized in automatic hearing and voice translation in electronics. Deep learning applications, for example, are used to power home help gadgets that respond to your voice and remember your preferences.

Artificial intelligence, machine learning, and deep learning development infographic with icons and timeline

Check out these links to learn more about Deep Courses:

  1. Introduction to Deep Learning: What Is Deep Learning?
  2. Deep Learning for Engineers
  3. Deep Learning with MATLAB: Deep Learning in 11 Lines of MATLAB Code

If you’re interested in technology feel free to check out our courses to help build the basics and foundations necessary to expand the creativity of this generation!

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