Let's understand what deep learning is. Deep learning is a part of machine learning that deals with emulating the learning approach of human beings. Traditional machine learning algorithms are linear in nature, whereas deep learning algorithms are nonlinear and stacked in a hierarchy of increasing complexity and abstraction, for example, when the parents of a toddler teach him what a cat looks like by pointing at it, and when he points to an object the next time and tags it as a cat, then the parents confirm by saying "Yes, it is a cat" or if the toddler identifies it wrongly, then the parents say "No, it is not a cat." In this way, a toddler learns to identify a cat and makes himself aware of all the features of a cat over a period of time. In this process, what a toddler does is unconsciously build a stack of information in their mind about a cat, where each layer of this information in the stack is created using the information from the previous layer. Every next layer of information becomes more complex and weighted than the previous one. A deep learning algorithm applies the same technique to learn and identify the content of images that are fed to it.

In traditional machine learning, the learning is supervised, where the programmer has to specifically define all the features of an object that the algorithm should look for and identify in an image that is fed to it as input. The programmer has to define the feature of a cat (object) and tag the cat in the training dataset. This process is called feature extraction, which is very time consuming. Also, the accuracy of the result depends on how efficiently the features are defined and the object is tagged in the input dataset.

The advantage of deep machine learning (or simply deep learning) over traditional machine learning is that the programmer is not required to do the feature extraction task by themselves. Here, the software program creates the feature list by itself; hence, deep learning is called unsupervised learning, which is not only fast but more accurate than supervised learning.

To implement deep learning, we need to provide the training dataset, which contains the images tagged as cat or no cat. Then, the program uses the input images to extract the features of a cat and build a model around it to predict a cat in a new dataset, which is untagged. This model is called the predictive model. The deep learning algorithm looks for patterns in pixels from digital image data and with each iteration, the predictive model becomes more complex and accurate. Unlike a toddler, who takes a long time to accurately identify a cat every time, the deep learning algorithm can do it in minutes. So, to achieve an acceptable level of accuracy, deep learning algorithms need huge volumes of data and processing power.

Since deeplearning techniques depict the human brain, they are sometimes called neural networks. There are different types of neural networks such as recurrent neural networks, convolutional neural networks, artificial neural networks, and feed forward neural networks. Each one of them has its own implementation and use cases.

Deep learning is mostly used in image recognition, natural language processing, and speech recognition tools.

The limitation of deep learning is that it knows only what has been taught to it. This means that if it is trained to identify a cat, then it cannot identify a dog by itself. And to identify the cat accurately, it needs huge volumes of pretagged training data, which is not an easy task. Additionally, there are issues of bias in the results because if the training dataset contains only a black cat, then it might not identify a white cat, which is a biased decision against white cats.