What is Deep Learning?
If such terms as “Machine learning” and “Artificial Intelligence” isn’t new for you that it would be easy to understand what is deep learning? To be able to better understand deep learning one knows what is machine learning?
In short, machine learning incorporates systems that keep on improving their performance in a given task based on more and more data or experience.
Deep learning is a machine learning technique that learns through an artificial neural network that acts very much like a human brain and allows the machine to analyze data in a structure very much as naturally come humans, learn by example. A deep learning model don’t require a human programmer to them what to do with the data.
Deep learning is a subfield of machine learning, which also is a subfield of Artificial Intelligence. Deep Learning uses a class of algorithms called artificial neural networks which is come from the biological neural network functions inside the brain. It is a key technology behind the self-driving car, enabling them to recognize a stop sign. It is also key voice control in consumer devices like phones, TVs, etc. It is getting lots of attention lately and for good reason.
Differences between deep learner and Machine learning:
The major difference between machine learning and deep learning is the ability of feature extraction. In practical terms, deep learning is just a subset of machine learning. It technically is machine learning and functions in a similar way, but it is different from each other.
- One of the most important differences between deep learning and machine learning is the way data is presented in the system or model. Machine learning algorithms almost always require structured data, while deep learning networks depend on artificial neural networks (ANN).
- Machine learning algorithms always “learn” to act by understanding labeled data and then use it to produce new results with more datasets. A deep learning model doesn’t require a human programmer to them what to do with the data.
- A deep learning algorithm takes a long time to train. This is because there are so many parameters in a deep learning algorithm machine learning comparatively takes much less time to train, ranging from a few seconds to a few hours.
- When solving a problem using a machine learning algorithm, it generally breaks the problem down into different parts, solve them one by one and combine them to get the result. Deep learning in contrast advocates solving the problem end-to-end.
- Both these subsets of AI revolve around data to deliver any form of “intelligence”. However, what should be known is that deep learning requires much more data than a traditional machine learning algorithm.
Applications of Deep Learning
Deep neural networks have touched every minute of different areas of human life and society. From language to medicine to robotics, it has revolutionized the way humans solve problems.
Alexa, Siri, or Cortana are an essential part of our daily life. These virtual assistants use deep learning to understand the human language and respond to our voice commands. It can easily understand natural language.
The use of autonomous vehicles has become a reality due to the advancements in deep learning algorithms. Many companies are working on autonomous vehicles, such as Google, Tesla, BMW, etc.
Chatbots are becoming more and more powerful because of natural language processing and big data. It is the future of digital communications and social interaction
The use of Deep Learning methods to perform the large datasets of faces and learn rich and compact representations of faces. In China, you can use facial recognition for online shopping and buying goods.
Google Translate is the best example of language translation. Deep learning is helping enhance the automatic translation of the text by using stacked networks of neural networks and allowing translations from images.
The impact of deep learning in the field of health care is very promising for improving patient care and reducing healthcare costs.
Future of Deep learning
In the near future, one thing that you will notice becoming famous and your daily driver would be decentralized artificial intelligence.
Deep learning (DL) has a big scope in the future and the biggest reason for it is that DL doesn’t require any kind of feature engineering. There is an ample opportunity to apply DL in the field of medicine, Language Translation, Automatic Speech Recognition, precision agriculture, etc. NVIDIA has increased the lifespan of DL by creating CUDA equivalent for Deep Neural Network (cuDNN). Thus DL has a scope to tackle a wide variety of problems in the near future. Deep learning not required to human program it learned by the model itself from data, it solves our biggest problem of feature engineering.
So, if you are looking for a future in Deep Learning, you are looking in the right direction.
We’ve reached the conclusion of our discussion. We hope you liked this article. If you have any questions on this topic or on how to become an DL professional, feel free to ask us. We’d love to hear from you.