Updated June 16, 2023
Introduction to Deep Learning
Deep Learning is one of the machine learning techniques by which we teach/train computers to do what humans do. For example, driving a car – deep learning plays a key role in driverless car technology by enabling them to identify different traffic signs, road signs, pedestrian signs, etc. Other key areas of deep learning are voice control in home systems, mobiles, wireless speakers, Alexa, smart TVs, etc. Deep learning is mostly about multiple levels of abstraction and representation by which a computer model learns to classify images, sounds, text, etc. Deep learning models achieve better accuracy and performance than humans in some models. These computer models are generally trained by a large set of data labeled and unlabeled to identify objects and neural networks, which have multiple layers in each network.
What are Deep Learning Techniques?
I will be explaining deep learning techniques in layman’s terms. In general, we will do two tasks all the time, consciously or subconsciously, i.e., categorize what we feel 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 a 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 crows will categorize a place to build their nest or not, a bee will decide on some factors when and where to get honey, and bats will come during the night and sleeps during morning based on the day and night categorization.
Let us visualize these tasks for categorization and prediction, and they will look alike as in the below image. For categorization, we are categorizing 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.
Categorization of Deep Learning
- In general, to categorize 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. How we categorize cats and dogs in our brains is much more complex than drawing a red line as above.
- We will categorize between two things based on shapes, size, height, looks, etc. 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 categorization into cats and dogs.
- Once we can categorize cats and dogs as children, we can categorize any dog or cat even if we didn’t see it before.
Prediction of Deep Learning
- For prediction based on the line, we draw through data points if we can predict where it is most likely to go upward or downward.
- The curve also predicts 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 in red 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, task categorization and prediction end at a similar point, i.e., drawing a curvy line from data points. If we can 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, called deep learning.
Examples of Deep Learning Techniques
As we know, deep learning and machine learning are subsets of artificial intelligence. Still, deep learning represents the next evolution of machine learning, as machine learning will work based on algorithms and human programs. In contrast, deep learning learns through a neural network model, which acts similarly 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 examples of deep learning in the real world.
- Computer Vision: Computer vision deals with algorithms for computers to understand the world using 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, natural language generation, etc.
- Autonomous Vehicles: Deep Learning Techniques Deep learning models are trained with a huge amount of data for street signs; some models specialize in identifying pedestrians, humans, etc., for driverless cars while driving.
- Text Generation: Using deep learning models, trained by language, grammar, types of texts, etc., can be used to create a new text with correct spelling and grammar from Wikipedia to Shakespeare.
- Image Filtering: Deep learning models, such as those used for adding color to black and white images, can accomplish the task more efficiently than manual methods.
Conclusion
Finally, it’s an overview of deep learning and its applications in the real world. After reading this article, I hope you will understand what deep learning is. 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 alpha Go 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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