While using most modern-day computer technology services or gadgets, have you ever wondered how certain things occur like how does Google translate functions so fast and accurate within milliseconds or YouTube or Netflix is capable to suggest movies to you that exactly suit your demand? Recent computer technological innovations like Artificial Intelligence and more often, core aspects of Artificial Intelligence like Machine Learning and Deep learning play a vital role in processing large, raw and complex data sets to produce suitable results or in some cases used to train a machine to mimic the human brain; the case of artificial intelligence.
It is these technological models behind the supercomputer; AlphaGo, driverless cars, Apple’s Siri, etc. They all function with the application of deep learning networks. In this article, we will get more insight into deep learning and its applications under the following guidelines:
- What is deep learning?
- How it works
- Deep learning methods
- Areas of application of deep learning
- Importance of deep learning
- How long does it take to train a learning model?
- Available frameworks
- Future of deep learning
What Is Deep Learning?
Deep Learning is a Machine Learning technique where computers use artificial neural networks and algorithms to perform human-like activities by using large amounts of data. Here, the computer sets up basic parameters about the data and then trains by performing repetitive processing or iteration and finally learn on its own by recognizing patterns after many layers of processing.
How Deep Learning Works
Unlike other machine learning techniques, this technological model addresses problem-solving from a different and perspective; this is more analytical. This machine learning technique rather tells the computer how to solve a problem than training the computer to solve the problem itself.
a. Deep Learning vs. Machine learning
In traditional machine learning, the learning process is supervised. This requires the programmer has to be extremely specific when telling the computer what types of things it should be looking for to decide if an image contains a cat or not. This is called feature extraction, and it is a laborious process which the computer’s success rate depends entirely upon the programmer’s ability to accurately define a feature set for “cat” to be used by machine learning methods or algorithms known as flat algorithms. Flat here because these algorithms cannot normally be applied directly to the raw data (such as .csv, images, text, etc.) unless it undergoes Feature Extraction.
Conversely, deep learning uses artificial neural networks hence no need for Feature Extraction since artificial neural networks can automatically extract features needed in learning from the data set. Here, a more and more abstract and compressed representation of the raw data is produced over several layers of artificial neural-nets. This compressed representation of the input data is then used to produce the result. Thus with this technology, a machine becomes more accurate with increase data input whereas traditional machine learning models such as SVM and Naive Bayes classifier stop improving after a saturation point.
b. How Machines Get Inspired By The Brain
Deep learning’s underlying principle is based on iterations or repetition, this is best exemplified in the case of a toddler whose first word is a cat. The toddler learns what a cat is after the parent keeps pointing to it and saying the word “cat” and the toddler learns through that repetition. In real life deep learning applications, the computer program is mostly provided with training data which can be a set of images for which a human has labelled each image “cat” or “not a cat” which are meta tags.
The program uses the information it receives from the training data to create a feature set for “cat” and builds a predictive model. In most cases, the computer first creates a predictive model that may say that anything in an image that has four legs and a tail should be labelled “cat.” However, unlike a toddler, deep learning algorithms are sophisticated and faster hence they can sort through millions of images, accurately identifying which images have cats in them within a few minutes.
Deep Learning Methods
Various different methods can be used to create strong learning models. These techniques include:
- Learning rate decay: this is the process of adapting the learning rate to increase performance and reduce training time.
- Transfer learning: involves perfecting a previously trained model hence depends on a preexisting network
- Training from scratch: here a developer collects a large labelled data set and configure a network architecture that can learn the features and model.
- Dropout: used in speech recognition, document classification and computational biology to solve the problem of overfitting in networks with large amounts of parameters.
Applications of Deep Learning
Deep learning may appear to be in a research or development phase, however, it has many practical applications that businesses are using in the real world. Some popular uses include:
- Image Recognition: During investigations, law enforcement agents use deep learning for automatic image captioning and scene description.
- Speech Recognition: in business and academics deep learning technologies is been used systems to recognize human speech and voice patterns.
- Recommendation Systems: in shopping and online platforms this technology can be used to enhance recommendations in complex environments.
- Natural Language Processing: Neural networks, a central component of deep learning, have been used to process and analyze written text for many years.
- Self-driving cars
- Language translation services
Importance of Deep learning
Today, machines are able to solve problems without human intervention. Below are certain tasks machines could learn to do after a continuous addition of data which in turn learns from the data patterns.
- In business, deep learning models are already being used in chatbots to improve customer experiences and increase customer satisfaction.
- Text generation: computers are being taught the grammar and style of a piece of text and are then using this model to automatically match the proper spelling, grammar and style of the original text.
- Aerospace and military: it is being used to detect objects from satellites that identify areas of interest, as well as safe or unsafe zones for troops.
- Industrial automation and security: used to ensure safety in factories by automatically detecting when a worker or object is getting too close to a machine.
- Adding colour: With this technology, black and white images or videos can be given colour.
- Medical research: deep learning has already be used by Cancer researchers to automatically detect cancer cells.
- Computer vision: it provides computers with extreme accuracy for object detection and image classification, restoration and segmentation.
How Long Does It Take To Train A Learning Model?
Training a deep learning model varies from hours to weeks or more, and is dependent on factors such as the available hardware, optimization, the number of layers in the neural network, the network architecture, the size of the dataset and more.
Available Learning Frameworks
A wide range of deep learning software frameworks exists which allows users to design, train and validate deep neural networks, using a range of different programming languages.
- TensorFlow software library provided by Google allows users to write in Python, Java, C++, and Swift, and that can be used for a wide range of deep learning tasks such as image and speech recognition.
- PyTorch is another popular choice best adapted for beginners and this framework offers an imperative programming model familiar to developers and allows developers to use standard Python statements.
- Other wide range of other options does exist like Microsoft’s Cognitive Toolkit, MATLAB, MXNet, Chainer, and Keras.
The Future of Deep Learning
Deep learning is one of the foundations of Artificial Intelligence (AI) and Machine Learning. Its core difference is the DEEP aspect of AI, meaning it analyses many layers repeatedly to create a pattern based on similarities. In effect, its techniques have improved the ability to classify, recognize, detect and describe – in one word, understand. Since its inception in the early 2000s, deep learning has evolved from mere algorithms through neural networks used in image classification to computation advancements and recently to human-to-machine interfaces; In this most recent technology, the mouse and the keyboard are being replaced with gesture, swipe, touch and natural language, bringing about a renewed interest in artificial intelligence and deep learning.
