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March 24, 2018

Deep Learning with MATLAB

What Is Deep Learning?

Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before.

In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labelled data and neural network architectures that contain many layers.

How Deep Learning Works?

The term “deep” usually refers to the number of hidden layers in the neural network. Traditional neural networks only contain 2-3 hidden layers, while deep networks can have as many as 150. Deep learning models are trained by using large sets of labelled data and neural network architectures that learn features directly from the data without the need for manual feature extraction.

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

One of the most popular types of deep neural networks is known as convolutional neural networks (CNN or ConvNet). A CNN convolves learned features with input data, and uses 2D convolutional layers, making this architecture well suited to processing 2D data, such as images.

CNNs eliminate the need for manual feature extraction, so you do not need to identify features used to classify images. The CNN works by extracting features directly from images. The relevant features are not pre-trained; they are learned while the network trains on a collection of images. This automated feature extraction makes deep learning models highly accurate for computer vision tasks such as object classification.

Example of a network with many convolutional layers. Filters are applied to each training image at different resolutions, and the output of each convolved image serves as the input to the next layer.

CNNs learn to detect different features of an image using tens or hundreds of hidden layers. Every hidden layer increases the complexity of the learned image features. For example, the first hidden layer could learn how to detect edges, and the last learns how to detect more complex shapes specifically catered to the shape of the object we are trying to recognise.

What’s the Difference Between Machine Learning and Deep Learning?

Deep learning is a specialized form of machine learning. A machine learning workflow starts with relevant features being manually extracted from images. The features are then used to create a model that categorizes the objects in the image. With a deep learning workflow, relevant features are automatically extracted from images. In addition, deep learning performs “end-to-end learning” – where a network is given raw data and a task to perform, such as classification, and it learns how to do this automatically.

Another key difference is deep learning algorithms scale with data, whereas shallow learning converges. Shallow learning refers to machine learning methods that plateau at a certain level of performance when you add more examples and training data to the network.

A key advantage of deep learning networks is that they often continue to improve as the size of your data increases.

Comparing a machine learning approach to categorising vehicles (left) with deep learning (right).

Deep Learning with MATLAB.

MATLAB makes deep learning easy. With tools and functions for managing large data sets, MATLAB also offers specialized toolboxes for working with machine learning, neural networks, computer vision, and automated driving.

With just a few lines of code, MATLAB lets you do deep learning without being an expert. Get started quickly, create and visualise models, and deploy models to servers and embedded devices.

  1.    MATLAB lets you build deep learning models with minimal code. With MATLAB, you can quickly import         pre-trained models and visualise and debug intermediate results as you adjust training parameters.
  2. You can use MATLAB to learn and gain expertise in the area of deep learning. Most of us have never taken a course in deep learning. We have to learn on the job. MATLAB makes learning about this field practical and accessible. In addition, MATLAB enables domain experts to do deep learning – instead of handing the task over to data scientists who may not know your industry or application.
  3. MATLAB enables users to interactively label objects within images and can automate ground truth labeling within videos for training and testing deep learning models. This interactive and automated approach can lead to better results in less time.
  4. MATLAB can unify multiple domains in a single workflow. With MATLAB, you can do your thinking and programming in one environment. It offers tools and functions for deep learning, and also for a range of domains that feed into deep learning algorithms, such as signal processing, computer vision, and data analytics.

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