The evolution of technology has taken humanity to heights like never before. Work areas of medicine, safety, learning, and providing other kinds of help has reached a peak. But it does not stop there. Artificial intelligence is the next big thing in the world of technology and computer science but to understand it, it’s important to know what it consists of. It is essential to know what is deep learning and what artificial neural network means.
The AI technology field is extremely advanced and interesting. These two tools that are being used in artificial intelligence are very powerful in terms of solving complex problems and to develop even higher standards in science.
It is safe to say that this kind of mechanism is a transition to the next level of technology. The companies of today have already recognized its importance and started using it in most of their cases. Let’s take Google for example. Google uses search engine AI to learn from its’ users. If you are looking for something in its search bar, for example, a “laptop computer”, and after getting the results you press on it, you just taught Googles’ AI that a “laptop computer” is what you pressed on. Wonder how does it work? Let’s dive deeper and find out.
Table of Contents
Understanding Deep Learning AI
Latest Coursera Coupon Found:
Selected Coursera Courses
Take advantage of 100% free online Degrees and Certificates on Coursera & master your skills. Follow this Coursera coupon link to access the courses.
What is Deep Learning technology so special about, that it is a technique for computers (AI) to learn just like humans do - by trial and error. If you are wondering if you have ever seen it before, you probably have. It is the technology behind such applications as voice control over devices like phones, tablets or television. Not so long ago we have been introduced to the driverless cars, which is also a product of deep learning. With the help of DL, artificial intelligence recognizes stop signs, pedestrians, and other obstacles in the road that might cause a disaster.
To perform such actions, a computer that is using deep learning techniques requests a large amount of training data (this is the work of neural networks, we will get to that a bit later). Such technological achievements like driverless cars need thousands of video footage and images to recognize every single situation for it to be safe. The recent improvements in Deep Learning have been taken to the level where it outperforms humans in a certain amount of tasks.
How Does It Work?
As already mentioned slightly above, what is deep learning using to perform such tasks are neural networks. Most of the times deep learning AI is referred to as a deep neural network. The word deep in this term stands for the layers that are hidden in the neural network.
Deep learning models are trained by getting a sufficient amount of data and neural network data architectures that learn features directly from the data without manual labor. Neural networks are systems that are connected just like our biological neural networks. These kinds of systems are created in a way to adapt to situational needs. Once the neural nets identify the results for a certain object, the next time the NN systems can identify whether it is the same object or not. The neural networks do not recognize objects the same way we do, it recognizes objects through their own unique set of features.
Artificial Neural Networks
One of the most common and popular types of what is deep learning using is known as conventional neural networks or CNN for short. It combines the learned features with input data, and uses 2D convolutional layers, making this architecture well suited to process 2D data. For example, it can be images or coordinate plane sheets.
Conventional neural networks work in a way that there is no longer a need for manual feature extraction. It extracts features directly from images. Artificial neural networks have an automated feature extraction that makes deep learning models picture-perfect accurate for computer vision tasks such as object classification.
CNN's learn to detect different features using numbers of hidden layers. Every number of the hidden layer increases the complexity of the learned image features. CNN's learn different features from every layer.
The Common Examples
According to sources, there are three most used ways to use deep learning to perform object classification:
- Transfer learning. The learning approach is mostly used in deep learning applications. It is done by having an existing network and adding new data to previously unknown classes. This way it is a lot better to save some time because instead of you reduce the amount of image processing. It allows categorizing only certain objects rather than going through all different objects until it finds the correct one.
- Training from nothing. This is mostly used for new applications that are going to have a large count of output categories. It begins by gathering a large number of labeled data sets and designing a network architecture that will learn the features. While transfer learning can take up to hours or minutes, this method takes a bit longer - from days to weeks to train.
- Feature extraction. Not as popular as the mentioned methods before, but still used commonly. This is a method that is used for a more specialized approach to deep learning. It uses the network as a feature extractor. Since the layers in conventional neural networks are tasked with learning certain features from images, it is also possible to withdraw these features and make it as an input to a machine learning model.
What Are Other Types of Neural Networks?
While the conventional neural network could be considered as the standard neural net that has been expanded across space using shared weights, there are also some different types.
A recurrent neural network, rather than the conventional one, is extended across time by having edges that feed into the next time step instead of the next layer in the same time step. This artificial neural network is used to recognize sequences, for example, a speech signal or a text.
Also, there is a recursive neural network. This NN system has no time aspect to the input sequence, but the input has to be processed hierarchically.
Neural Networks in Action
It might get tricky when trying to understand what are the real benefits of the neural networks in real-life situations. Artificial neural networks are very popular among stock market experts. With the help of NN systems, it is possible to apply “algorithmic trading”, that can be applied to the likes of financial markets, stocks, interest rates, and various currencies. Neural network algorithms can find undervalued stocks, improve existing stock models, and use deep learning to find ways how to optimize the algorithm as the market changes.
Since neural networks are very flexible, they can be applied in various complex pattern recognitions and predict problems. As an alternative to the example above, the NN system can be used to forecast business, detect cancer from images, and recognize faces on social media images.
Deep Learning in Action
Not only neural networks have real-life examples. Deep Learning can also be described as some of the following creations:
- Virtual assistants.
- Chatbots or service bots.
- Personalized shopping and entertainment.
- Imagine colorization (uses algorithms to recreate true colors on images that are black-and-white)
- Simplistic design (no unnecessary information)
- Good quality of courses (even the free ones)
- A few different features to choose from
- Nanodegree Program
- Suitable for enterprises
- Paid Certificates of completion
What Are The Key Differences Between DL and NN?
With all this information it is clear that Deep Learning and Neural Networks are strongly connected and probably wouldn’t work well when separated. To be able to understand what is deep learning and what is neural networks it is essential to know the main takeaway.
Neural networks transmit data in the form of input values and output values. It is used to transfer data by using connections. Whereas Deep Learning is related to the transformation and extraction of feature which attempts to establish a relationship between stimulus and associated neural responses present in the brain. In other words, Neural Networks are used for natural resource management, process control, vehicle control, decision making, while Deep Learning is used for automatic speech recognition, image recognition, etc.
To sum up, Deep Learning and Neural Network complete each other and will develop into even bigger technological wonder than it is today. Head over to our courses page and take a course on Machine Learning applications. Artificial intelligence is the next step in our age, and the more experience it gets, the more benefits it will provide to society.