Deep Learning is part of machine learning and a subset of artificial intelligence. Artificial intelligence allows machines to copy human behavior whereas machine learning uses its algorithms to achieve artificial intelligence. Deep Learning is a type of machine learning with structure same as the human brain. In Deep Learning, we can call it an Artificial Neural Network.
A driverless car is one example of deep learning. It enables car in a way that it can recognize stop sign, pedestrian and traffic signals. Deep Learning is achieving things that were not possible previously and of course, that independence comes only because of high-value data.
In deep learning neural network, there are input layer, hidden layer, and output layer. The main difference between simple neural network and deep learning neural network is that- in a simple neural network, there is only one hidden layer but in deep learning neural network there are more than one (deep) hidden layers.
The input layer receives nonlinear transformations as input data. Mathematical computations are performed on input data in the hidden layer. The hidden layer repeatedly performs computation on input data until it sends accurate data to output. For Deep Learning, one of the challenge is deciding no. of hidden layers and neurons for each of them.
Customer support: nowadays most people contacting customer support for any business and that conversation seems real where from another side there is bot assisting them. Deep learning is being used in many businesses to improve customer satisfaction and it results in improving businesses.
Healthcare: In healthcare, deep learning models can detect cancer cells and give detailed results.
Self-driving cars: It is an example of science changing into reality. This vehicle can detect signals, speed breakers, and even pedestrian paths too. Tesla and Nissan are the companies that are trying to work on these self-driving cars.
Text generation: with the use of deep learning models, Machines can read and analyze grammar and style of text and then can automatically create an entirely matching new text with proper spelling and style of the original text.
Military and Aerospace: Deep learning models can detect satellite objects and it can also detect unsafe and safe zones.
Industrial automation: one of the useful applications of deep learning is it is used to detect if the worker is getting too close to the machine. This application is used in lots of factories and improving the safety of workers. Adding color to Photos: previously, applying color to photos was a manual and longtime task but now, deep learning models can be used to add your favorite color to black and white photos and videos in very less time.
Deep learning models can learn through observations and this becomes the biggest disadvantage for them. They can only know what was there in data which was applied to get trained. With a small amount of data, the models will not learn the inaccurate way.
In Deep learning, if a model learns on data which includes biases, the model will replicate those biases in its output predictions also.
Deep learning models require hardware such as processing unit and high-performing GPUs to improve productivity and to decrease time consumption. This hardware is high cost and needs lots of energy for working.
One biggest limitation of Deep learning is the requirement of a huge database. If you want to produce the most accurate output and prediction you will require more amount of data in the input. Deep learning models cannot handle multiple tasks at the same time. They can generate accurate solution but for one particular problem only. To solve a similar problem again, they need retraining.
Deep learning is a popular term in the computer field. A career in Artificial intelligence, machine learning, and deep learning are booming nowadays. Enormous job opportunities are getting created every day for these fields but demand for deep learning is high for sure, as per the reports.
For deep learning professionals, the average salary range in the US is approximately $70,000 to $75,000/year.
There is a high demand for careers in deep learning and AI as many jobs are available for this field with a good salary. As a result, most of the professionals are trying to get trained in deep learning. NearLearn provides Deep Learning training in Bangalore at affordable prices. We also provide Machine learning certification and Python Training in Bangalore. Online training and corporate programs are also available for the same.
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