How deep is “Deep Learning”?

Deep learning is a class of machine learning algorithms that uses complicated layers to progressively cite higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts related to a human such as numerals or letters or faces.

Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks, and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection, and board game programs, where they have produced results analogous to and in some cases superior to human experts.

If you are just starting in the field of deep learning or you had some experience with neural networks some time ago, you may be confused. I know I was confused initially and so were many of my colleagues and friends who learned and used neural networks in the 1990s and early 2000s.

The leaders and experts in the field have ideas of what deep learning is and these specific and nuanced perspectives shed a lot of light on what deep learning is all about.

Why is it called deep learning?

Deep Learning is all about Neural Networks. The formulation of Neural Networks is inspired by the human brain. The human brain consists of billions of neurons interconnected to each other. Each neuron receives the signal, processes the signal, and passes it on to the other neurons. This is how the information is passed on in our brains.

Furthermore, Neural Networks contains 3 layers i.e. input layer, hidden layer, output layer. Each layer has neurons interconnected to the neurons in the next layer.

  • Input layer: This is the layer in which you feed the input. The number of nodes in this layer depends on the number of dimensions of the data.
  • Output layer: This is the layer in which the output is released. The number of nodes depends on the number of classes that you have.
  • Hidden layer: This is like a black box. This is the layer in which the feature extraction takes place. The number of hidden nodes and the number of hidden layers is arbitrary. Every hidden layer extracts features that help in identifying images if you’re classifying the images. The first hidden layer may extract peculiarities such as edges. The second hidden layer builds upon the peculiarities extracted from the first layer and may extract features related to objects. The third layer might extract peculiarities related to different faces and so on.

“Why deep?  

The deeper you go, the more complex features are extracted.”

Importance of Deep learning.

Accuracy is what separates deep learning from other technologies. Deep learning attains recognition accuracy at a very high level than ever noticed before. This allows consumer cybernetics to meet the expectations of the user, and it is most significant for applications where the sanctuary is a major concern, like driver-less cars. The recent innovations in deep learning have enhanced to the point wherein few tasks such as classifying objects in images, deep learning outperforms humans.

In contrast, a machine learning program depending on labels to process data requires those labels put in place from a person. For a person to label all this data, it requires a lot of energy and labor. This process is costly and proves to be time-consuming. Deep learning can learn from structureless data. Researchers are, therefore making it valuable to practical applications in the real world.

Deep learning is concerning because it creates a new level of communication between humans and computers. It possesses fundamental functions that make machines work without yoke on a human. The duties of deep learning enable it to remain in a learning process during its use. This development process is so that it will reshape organizations and industries over time in undiscovered ways. It is also affecting the jobs available in ways that are questionable as advantages or disadvantages to the average person. Therefore, managers, owners, and employers will benefit from developments and innovations in deep learning. This knowledge helps them know where the market is heading.

Real-World Applications of Deep Learning:

  • Speech Recognition
  • Image Recognition
  • Art
  • Toxicology and Drug Discovery
  • Video Games

Deep Learning Process:

Most of the deep learning methods make use of neural network architectures. Because of this, deep learning models are usually referred to as deep neural networks. The term “deep” generally points out the number of hidden layers in a neural network. The deep networks can have around 150 hidden layers while traditional neural networks contain only 2 to 3 hidden layers. The models of deep learning are trained with the help of neural network architectures and huge sets of labeled data that learn features from the data directly without requiring manual feature extraction.

Neural networks are organized in layers and consist of a set of interconnected nodes. Networks can contain tens or hundreds of hidden layers.

One of the most popular kinds of deep neural networks is CNNs. A Convolutional Neural Network convolves input data with learned features and utilizes 2D convolutional layers, thus making this architecture highly suited to processing 2D data, like images.

Convolutional Neural Networks eliminates the need for feature extraction manually. So, you don’t need to detect features utilized to classify images. The convolutional neural network works by directly extracting features from images. The relevant features are learned but not pre-trained while the network trains on a collection of images. Thus, this kind of automated feature extraction enables deep learning models to be highly accurate for computer vision tasks like object classification.

What does 2020 hold on deep learning?

It is 2020, and deep learning is indeed proving to be useful to consumers. The benefits are numerous, and it promises a future when humanity can work concurrently with machines to create and improve on our world. But, all is not gold, and deep learning is not solely consigned worldwide. The potential for hazards is daunting in deep learning. The use of this technology is not secured, and the machines can sometimes convincingly deceive humans.

The future of deep learning is particularly bright! The great thing about a neural network is that it outshines at dealing with an immense amount of diverse data. That’s especially relevant in our era of advanced smart sensors, which can gather an improbable amount of information. Traditional computer solutions are beginning to grapple with sorting, labeling and concluding so much data.

Recently, the deep fakes that surfaced on the internet are an example of this. Videos can now be produced using the image and likeness of an individual with the use of deep learning programs. The flaws are minimal, and this technology is already here. The access might not yet be widespread. But, the wrong people with knowledge of its potential can prove to be catastrophic. But, as it stands, the good outweighs the bad, and the importance of deep learning continues to grow.

Deep learning, on the other hand, can deal with the digital mountains of data we are gathering. In fact, the larger the amount of data, the more efficient deep learning enhances compared to other methods of analysis. This is why organizations like Google invest so much in deep learning algorithms, and why they are likely to become more common in the future.

“And, of course, the robots. Let’s never forget about robots.”


Deep learning can equal or transcend expert human performance in machine vision, speech recognition, and predictions we have given machines ayes eyes and ears so the world starts asking itself about the business application of deep learning. The deep learning article aimed to give you a brief introduction to the field of deep learning algorithms, beginning with the most basic unit of composition and progression through various ideas that are generated in deep learning fields. I believe you got to grasp something more major about deep learning.