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 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 car( 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 is deep learning in layman term 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, bat will come during 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 case of prediction we draw a line through data points to predict when it will increase and decrease.
1)Categorization
- 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 purpose 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 new-born 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 able to categorize any dog or cat even we didn’t see it before.
2)Prediction
- 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 to the curve.
- The data points which are in red color 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 tasks categorization and prediction are ended at 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 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. As machine learning will work based on algorithms and programs developed by humans whereas deep learning learns through a neural network model which acts like similar to humans and allows machine or computer 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 of deep learning in the real world.
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Computer Vision:
Computer vision deals with algorithms for computers to understand the world using an image and video data and tasks such as image recognition, image classification, object detection, image segmentation, image restoration etc.
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Speech and Natural Language Processing:
Natural language processing deals with algorithms for computers to understand, interpret, and manipulate in 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.
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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.
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Text Generation:
By using deep learning models which 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.
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Image filtering:
By using deep learning models such as adding color to black-and-white images can be done by deep learning models which will take more time if we do manually.
Conclusion
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 is deep learning 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.
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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:
