Introduction To Deep Learning
Deep learning is a subset of machine learning in artificial intelligence, i.e., based upon artificial neural network and representation learning, as it is capable of implementing a function that is used to mimic the functionality of the brain by creating patterns and processing data. Deep Learning is also used for decision-making in fields like driverless cars ( to detect pedestrians, street lights, other cars, etc.), speech recognition, image analysis( e.g., Identifying cancer in blood and tumors), smart TV, etc.
What is Deep learning?
I will be explaining what deep learning is in layman terms as below: In general, we will do two tasks all the time consciously or subconsciously, i.e., categorize what we felt through our senses (like feeling hot, cold mug, etc.) And prediction, for example, predicts the future temperature based on the previous temperature data. We do categorization and prediction tasks for several events or tasks in our daily life such as below:
- Holding Cup of Tea/Water/Coffee etc., which may be hot or cold.
- Email categorization such as spam/ not spam.
- Day-light time categorization such as day or night.
- Long-term planning of the future based on our current position and things we have – is called prediction.
- Every creature in the world will do these tasks in their life, for example, consider animals like crow will categorize a place to build its nest or not, a bee will decide on some factors when and where to get honey, the bat will come during the night and sleeps during morning based on day and night categorization.
Let us visualize these tasks categorization and prediction, and they will look alike as in the below image; for categorization, we are doing categorization between cats and dogs by drawing a line through data points, and in the case of prediction, we draw a line through data points to predict when it will increase and decrease.
- In general, to categorize between cats and dogs, or men and women, we don’t draw a line in our brains, and the position of dogs and cats is arbitrary for illustration purposes only, and it is needless to say the way we categorize between cats and dogs in our brains is much complex than drawing a red line as above.
- We will categorize between two things based on shapes, size, height, looks, etc., and sometimes it will be difficult to categorize with these features such as a small dog with fury and a newborn cat, so it is not a clear-cut categorization into cats and dogs.
- Once we are able to categorize between cats and dogs when we are children, then onwards we are able to categorize any dog or cat even we didn’t see it before.
- For prediction based on the line, we draw through data points if we are able to predict where it is most likely to go upward or downward.
- The curve is also a prediction of fitting new data points within the range of existing data points, i.e., how close the new data point is to the curve.
- The data points which are in red colour in the above image (right side) are examples of both within and beyond the range of existing data points, and the curve attempts to predict both.
Finally, both task categorization and prediction are ended at a similar point, i.e., drawing a curvy line from data points. If we are able to train the computer model to draw the curvy line based on data points we are done with, then we can extend this to apply in different models such as drawing a curvy line in three-dimensional planes and so on. The above thing can be achieved by training a model with a large amount of labeled and unlabelled data, which is called deep learning.
Examples of Deep learning
As we know, deep learning and machine learning are subsets of artificial intelligence, but deep learning technology represents the next evolution of machine learning. Machine learning will work based on algorithms and programs developed by humans, whereas deep learning learns through a neural network model which acts similar to humans and allows machines or computers to analyze the data in a similar way as humans do. This becomes possible as we train the neural network models with a huge amount of data as data is the fuel or food for neural network models.
Below are some of the examples in the real world:
- Computer Vision: Computer vision deals with algorithms for computers to understand the world using the image and video data and tasks such as image recognition, image classification, object detection, image segmentation, image restoration, etc.
- Speech and Natural Language Processing: Natural language processing deals with algorithms for computers to understand, interpret, and manipulate human language. NLP algorithms work with text and audio data and transform them into audio or text output. Using NLP, we can do tasks such as sentiment analysis, speech recognition, language transition, and natural language generation, etc.
- Autonomous vehicles: Deep learning models are trained with a huge amount of data for identifying street signs; some models specialize in identifying pedestrians, identifying humans, etc., for driverless cars while driving.
- Text Generation: By using deep learning models trained by language, grammar, and types of texts, etc., can be used to create a new text with correct spelling and grammar from Wikipedia to Shakespeare.
- Image filtering: By using deep learning models such as adding color to black-and-white images, it can be done by deep learning models, which will take more time if we do it manually.
Finally, it’s an overview of deep learning technology, its applications in the real world. I hope you will have a good understanding of what deep learning is after reading this article. As we know today, image recognition by machines trained by deep learning in some cases is better than humans, i.e., in identifying cancer in blood and tumors in MRI scans, and Google’s alphaGo learned the game and trained for its ‘Go’ match by training its neural network by playing against it over and over.
This has been a guide to What Is deep learning. Here we have discussed the basic concepts and examples of deep learning. You may also look at the following articles: