Deep learning is a machine learning technique that teaches computers to do what humans do naturally. It means that just like a child, they should do the learning process over and over again and increase their performance every time they try.
Let’s explore this technology rapidly.
Deep learning is one of the subfields of machine learning. Systems designed based on this technology, like humans, can offer solutions to specific issues and have continuous learning.
While machine learning deals with simpler concepts, deep learning uses artificial neural networks to understand and learn how humans think. Machine learning is about designing computers that require less human intervention in their work, while deep learning is about building computers with the ability to model the way humans think.
How does deep learning work?
Deep learning takes advantage of the hierarchical level in Artificial Neural Networks to perform the machine learning process. While in traditional applications, analytics are generated linearly with the data, deep learning enables machines to process the data non-linearly using a hierarchical function.
Computer models learn to perform specific tasks, such as identifying predefined objects in images, text, or audio. Deep learning can have very high accuracy that sometimes exceeds the human level. Deep learning algorithms are trained using a large set of labeled data and a neural network architecture consisting of several layers.
If you have no prior knowledge of neural networks, the definition of deep learning may be a little confusing. So let’s get to know these networks first.
What is artificial neural network?
Artificial neural networks are a set of algorithms whose model is inspired by the human brain. Neural networks are designed to recognize and clean patterns. These networks interpret the sensory data through some kind of machine perception, labeling or clustering of the raw input. The patterns that neural networks recognize are in the form of vectors of numbers, and data such as image, sound or text must be translated into these numerical vectors.
ANNs consist of a number of hidden layers through which the data is processed, allowing the machine to delve deeper into its learning, making connections and weighting the input to achieve the best results. The term “deep” refers to the number of layers in a neural network. While deep neural networks can have up to two hundred layers, traditional neural networks have only a few layers, typically around three.
Deep learning methods often use neural network architectures. This is why they are often referred to as “deep neural networks”. Deep learning models are trained by being fed large amounts of data and using a neural network architecture that learns features directly from the data without the need for manual input from the observer.
In deep learning networks, each layer of nodes is trained on a distinct set of features based on the output of the previous layer. As you progress through the neural network, your nodes can recognize more complex features as they aggregate and combine features with the previous layer. This issue is known as feature hierarchy, which expresses the hierarchy of increasing complexity for each layer.
Learning by neural networks is done using the data that is considered for training the network. That is, at the beginning, inputs are given to the network whose output is already known. The goal is to modify the aforementioned weights and bring the network output values closer to the predetermined outputs.
This happens by modifying the weights, and with each training session, the output of the network will become more accurate. Training continues until the network provides accurate and appropriate performance when faced with new and previously unseen data.
Let’s take a clear example.
Suppose a neural network wants to recognize photos that contain dogs. Note that, contrary to our imaginations, not all dogs are exactly alike. In addition, the pictures of the dogs were taken at different angles and with different light and shadow. Therefore, a training set of images containing many examples of dog faces labeled “dog” should be prepared. Also, a series of images of non-dog objects with the label “not dog” should be prepared.
Finally, the images that enter the neural network are converted into data. This data moves through the network and different nodes assign weights to different elements. Then the final output layer collects information such as having fur, muzzle, four-leggedness, etc. and provides the desired output: “Dog”.
Now the response generated from the neural network with the label is compared by the human. If it matches, the output is validated. Otherwise, the neural network notes the error and adjusts the weights. The neural network repeatedly adjusts its weights and tries to improve the dog’s detection skill. This teaching method is called supervised learning. This method happens when neural networks know nothing about what makes a “dog”; Rather, they should recognize the patterns in the data over time and learn on their own.
Deep learning has given us extraordinary discernment. But using this technology in the past was not as easy as it is today. This has two main reasons:
- Deep learning requires a huge amount of labeled data (labeled data means data that belongs to a certain category); For example, in the development of self-driving cars, billions of images and thousands of hours of video are needed to train a good and reliable system, which is now easier to obtain and access.
- Deep learning requires significant processing power. High-performance GPUs use a parallel architecture that is efficient for deep learning. With access to cloud computing technology in today’s era, development teams can reduce their system training time from weeks to a few hours or even less.
But what is the use of deep learning and how has it changed our lives?
The question may arise as to what effect this complex and expensive technology has on people’s lives. You should know that nowadays most of our daily affairs would not be possible without deep learning.
All that we know today as search engines, recommender systems, face and voice recognition systems, natural language processing systems, image processing systems, control systems for airplanes and self-driving cars, and many other things, they work based on deep learning.
There are many other uses for deep learning, which we rapidly explain here:
- Deep learning models have changed the world of detectives. They can detect fraud and identify unusual user transactions.
- Machine learning algorithms gave birth to a new field such as natural language processing, and thus machine translation, plagiarism detection, and sentiment analysis tools were born.
- Virtual assistants or software that provide services based on commands or questions for a person are formed and developed with deep learning models. Deep learning is used to gain experience by executing each human command and provide better service next time.
- Fake news and spam generated by robots can be identified using systems equipped with deep learning technology.
- Detecting wild animals, predicting weather conditions, and predicting crop yield in agriculture are some of the uses of deep learning.
- Deep learning is heavily used in the development of humanoid robots. Using deep learning, robots can receive updates on their behavior and performance at any moment. For example, they learn to change their path to another direction when facing an obstacle. They also understand the tone of human speech and our emotions.
- Deep learning has also changed the world of marketing and sales. Consumer behavior is changing over time. Many factors influence this issue, such as the time of year we are in, new products, fashion trends, popular videos that make something suddenly popular, etc. Social media and marketing campaigns produce a lot of data that can be used to understand customer preferences and behavior. Using this technology, new products can be offered to customers based on their purchase history to increase the profitability of companies and stores.
- Social networks use artificial intelligence and deep learning to establish a certain order. Twitter uses deep learning algorithms to analyze data using a neural network and learn about user preferences and interests.
Instagram uses deep learning to prevent cyberbullying and remove offensive comments.
Facebook uses deep learning to suggest pages, friends and products according to the interests of its users.
They also use artificial neural network for face recognition, which makes it very easy to tag friends in posts.
- Artificial intelligence is saving human lives today. The early diagnosis of diseases and the discovery of new drugs, as well as the detection of how viruses work and how to destroy them, are part of the power of deep learning technology in the field of medicine and health.
- Deep learning has led to the emergence of new weapons that will change the nature of wars in the future. This technology is now used in target detection and missile guidance.
- Automated or self-driving cars use deep learning algorithms to recognize stop signs, living beings, or other vehicles. When these cars receive more data from this method, they will be able to behave like humans and drive comfortably.
These are just some of the capabilities of deep learning. As you can see, deep learning algorithms have penetrated deep into our lives and you may be using the benefits and capabilities of deep learning every day without even realizing it.
Pros and Cons of deep learning at a glance
Deep learning has advantages over other branches of artificial intelligence. We take a brief look at them.
Automatic learning of features
Computers using machine learning models can automatically identify and learn from data features. Therefore, many problems that need to be solved are solved using deep learning-based systems.
Managing large and complex data
Processing large and complex data has always been a challenge for human resources, and deep learning has been able to provide a suitable answer to this need. The scalability of deep learning to manage a large amount of data is one of the most important advantages. Due to this important advantage, machine learning plays an important role in abstract and hierarchical representations of data.
Prediction of events and trends
Deep learning algorithms can protect sequential data and this feature can be used to predict future events. Forecasting the weather, increasing the stock market index and the profitability of organizations in the following months are part of the obvious capabilities of this branch of artificial intelligence.
As many hierarchical layers are used for non-linear information processing, the performance of deep learning algorithms is extended and enhanced. As more information is provided to these algorithms, its performance improves and the outputs are processed more accurately.
At the same time, this technology also has disadvantages. however, these do not ultimately prevent us from using deep learning.
The need for expensive hardware equipment
Working with massive data requires a lot of storage space and high processing power in hardware. These equipments are expensive and their operation takes time.
The need for high quality data
The performance of machine learning models depends on large and high-quality data. If these data are incomplete or limited, the approach of the models changes and they cannot reach the solution.
Being limited on issues
Deep learning models can only process on data that has already received relevant inputs. If there are small issues whose input is new, these systems cannot perform appropriately according to the new situation.
Difficult to identify and understand effective factors
When learning models rely on a large amount of data, it is difficult to identify the factors affecting the outputs. This is important when analyzing data that plays an important role in predicting events.