Since the invention of computers, many people asked one mind-boggling question: can computers mimic the human brain? For a long time, it seemed impossible, or like a story for a sci-fi movie. But with the artificial intelligence application’s unexpectedly quick rise, it seems more probable now. If one day computers will be able to do that, deep learning will play a very important role in that ability, because its main purpose is to mimic the human brain, as a part of the machine learning field.
Deep learning definition
Deep learning is a subfield of machine learning, which is a subset of artificial intelligence. In simplest terms, deep learning simulates human brain behavior with a neural network with multiple layers. The neural network is designed by using the structure of the human brain. These neural networks can identify patterns and classify different types of information. Similar to the human brain, deep learning algorithms aim to focus on drawing conclusions from a massive amount of data.
Equal to the amount of data deep learning algorithms train on, it requires a tremendous amount of computing power. Most organizations prefer high-performance graphical processing units for deep learning solutions because they can handle a large volume of calculations with their multiple cores and copious memory available. On the other hand, managing GPUs on-premises can also require a large amount of internal resources and is costly to scale.
How does deep learning work?
Deep learning works similarly to the human brain and is based on its structure. It mainly focuses on identifying patterns and classifying different types of information. To mimic the human brain, deep learning algorithms use neural networks. Deep learning neural networks, which can also be referred to as artificial neural networks, are made of layers of artificial neurons, which work together. These artificial neurons are software modules, also known as nodes. They use calculations to process data. With these nodes, artificial neural networks can solve complex problems. Each layer of the network can be considered as a filter that aims to increase the accuracy of the output.
Deep learning networks can learn by discovering structures only from the data they can gather. Layers in the interconnected nodes build upon the previous layer to improve its prediction or categorization. This process is called forward propagation. The deep learning model ingests the for processing from the input layer and provides the final prediction or classification in the output layer. Thus, the input and output layers are called visible layers.
What are the most common types of deep learning?
There are two types of deep learning algorithms that use different types of neural networks designed for specific problems or datasets.
A recurrent neural network is a type of artificial neural network that uses sequential data or time series data. These algorithms are mainly used for ordinal or temporal problems, such as language translation, natural language processing, speech recognition, and image captioning. Google Translate, Siri, and other voice search services are using recurrent neural network algorithms.
Convolutional neural networks are mainly used in computer vision and image classification applications. It uses nodes that are connected to one another and each of those nodes has an associated weight and threshold. Once the output of an individual node is above the threshold, it is activated and sends the data to the next layer. Three main types of convolutional neural networks are the convolutional layer, pooling layer, and fully-connected layer. It is capable of detecting features and patterns in an image, enabling object detection and recognition. Convolutional neural networks managed to win against a human in an object recognition challenge for the first time in 2015.
What is the difference between deep learning and machine learning?
First of all, deep learning is a subfield of machine learning. The main difference between classical machine learning and deep learning is that machine learning uses structured and labeled data to make predictions. In other words, in classical machine learning, input data’s specific features are defined for the model and organized into tables. If it should use unstructured, it pre-processes it to organize it into a structured format.
Deep learning eliminated the pre-processing, thus deep learning algorithms are capable of ingesting and processing unstructured data, such as texts and images. Deep learning also automates feature extraction to remove human dependency. Thus, deep learning can perform end-to-end learning. However, both machine learning and deep learning can support supervised learning, unsupervised learning, and reinforcement learning.
Deep learning’s capabilities offer various benefits over machine learning. Most importantly, deep learning can process and comprehend unstructured data. Deep learning applications can also analyze large amounts of data more deeply to reveal new insights. Deep learning models can also be improved based on user behavior and they can categorize volatile datasets.
What are the use cases of deep learning?
There are multiple use cases of deep learning in various fields, including automotive, manufacturing, medical, and many others. In general, we can categorize use cases of deep learning into four categories, computer vision, natural language processing, speech recognition, and recommendation engines.
- Computer vision is a technique to extract information and insights from images and videos. It is used in facial recognition, content moderation to remove inappropriate content, and image classification.
- Speech recognition is used to analyze human speech. It not only understands the words but also analyzes speech patterns, tone, and accent. Speech recognition is mostly used in call centers, converting clinical conversations, and creating subtitles for videos and meeting recordings.
- Natural language processing is a method to gather insights and meaning from texts and documents. By processing human-created texts deep learning can be used in automated virtual agents, summarizing articles automatically, and indexing key phrases to determine positive and negative comments.
- With deep learning methods, applications can track user activity to develop personalized recommendations. By analyzing human behavior applications aim to provide relevant recommendations to users.
Why is it called deep?
The term “deep” in deep learning refers to the hidden layers in the neural network. While traditional neural networks mostly contain 2 or 3 hidden layers, deep networks can have up to 150 layers.