In this article, we will discuss deep learning with examples and its applications from the real world.
What is Deep Learning
Deep learning and machine learning fall under the types of artificial intelligence (AI) . Deep learning, which mimics the way humans acquire specific types of knowledge, is an important element of the discipline of data science, which includes statistical, and predictive modeling.
Example
Deep learning is a part of AI (Artificial Intelligence) behind driverless cars, unable to recognize a stop sign, Deep learning utilizes both structured and unstructured data for training. The Practical examples of deep learning are a vision for driverless cars, Virtual assistants, money laundering, image recognition, text recognization, translation, and many more
Why deep learning?
- First of all, comes artificial intelligence and within artificial comes machine learning which is the cause part and within machining comes deep learning. Deep learning is a subfield of (artificial intelligence) AI. First of all, let’s talk about the general thing, why we use Deep learning and see what they use because, in machine learning, you guys must have seen that small data is used, smart states exist while Deep learning works on large amounts of data. Machine learning is not used for large data sets. Only a Large amount of data sets used deep learning.
- deep learning extract feature automatically Manually you don’t have to add much while in a machine you don’t have to do all the work by yourself.
Where this applied:
The deep learning algorithm is basically used in the medical field to check tumor cancer cells. The huge amount of data encountered in a real-world environment is extremely challenging for existing robotics control algorithms to handle. This necessitates the use of deep learning algorithms, which are able to learn controls given data. However, most conventional learning algorithms require hand-designed parameterized models and features, which are infeasible to design for many robotic tasks. Deep learning algorithms are general non-linear models which are able to learn features directly from data, making them an excellent choice for such robotics applications. Deep learning is currently used in computer science or data science most common picture recognition tools, are natural language processing (NLP) or speech recognition and translation software. These tools are starting to appear in applications as diverse as self-driving cars and language translation services. Self-driving car If you look inside a car, the whole system has been used deep learning so that you don’t need a driver inside it. It will automatically start driving what your car is because inside it. Yes, we have used the algorithm to see it, it knows that there is a straight road ahead. Deep learning is the most important technology that enabled self-driving cars. It’s a versatile tool that can solve almost any problem. It can be used in physics, for example, in the proton-proton collision in the Large Hadron Collider, just as well as in Google Lens to classify pictures. Deep learning is a technology that can help solve almost any type of science or engineering problem. The top application of deep learning is following across the industry.
Also Read: What is CNN in Deep Learning? Complete Guide
- Medical
- Self-driving cars
- Robotics
- Translation
- Visual recognition
- Fraud detection
- Photo description
- Deep Dreaming
- Automatic handwriting
- Automatic searching
- Finding accurate
Conclusion
Deep learning helps computers and humans to derive meaningful data and information from a plethora of data and make sense of structure and unstructured data. Here, the mathematics and statistics algorithms are combined with a lot of raw and fact-strong hardware to get strong full information. Keep visiting Deep Learning Sciences.