Introduction To Deep Learning

In this article you will learn What Deep Learning is. Furthermore, we will go over what a neural network is in machine learning and what are Deep Learning Use Cases. Finally, we will look at Deep Learning Applications.
Are you ready? let’s dive in into Deep Learning.

What exactly is Deep Learning?
Because machine learning is primarily concerned with addressing real-world issues. It also necessitates a few artificial intelligence concepts. Furthermore, neural networks are used in machine learning. That are intended to simulate human decision-making abilities.
The two most important restricted subsets are machine learning tools and methodologies. This solely emphasizes deep learning. Furthermore, we will use it to solve any complex problem that requires thought- human or artificial.

Any Deep neural network will consist of three types of layers:

The Input Layer

The Hidden Layer

The Output Layer

1.The input layer: It receives all the inputs and the last layer is the output layer which provides the desired output.

2. Hidden Layers: All the layers in between these layers are called hidden layers. There can be n number of hidden layers. The hidden layers and perceptron’s in each layer will depend on the use-case you are trying to solve.

3. Output Layers: It provides the desired output.

Deep learning is used to feed a computer system a large amount of data. The system then utilizes this data to generate a decision about other data. This data feeding occurs via neural networks. Furthermore, Deep Learning is critical since it focuses on the development of these networks.

What is Neural Networks?

It’s a lovely biological programming paradigm. In addition, it allows a computer to learn from observational data. It also offers the best answers to a variety of difficulties, image recognition, audio recognition, and natural language processing are examples of these technologies.

Deep Learning Use Case

In this use case, we’re passing high-dimensional data to the input layer.

The input layer will be required to match the dimensionality of the input data. This has several sub-layers of perceptions in order to consume the whole data.
Patterns received from the output layer will be stored in the input layer. It can also recognize the edges of pictures based on their contrast levels.
This output will be forwarded to the first hidden layer. And in this layer, it will be able to recognize numerous physical traits such as eyes, nose, ears, and so on.
Now, this will be fed to the second hidden layer where it will able to form the entire faces. Then, the output of second layer is sent to the output layer.
Finally, the output layer performs classification. This is based on the result obtained from the previous and predicts the name.

Deep Learning Applications

Now Let’s look at some Applications of Deep Learning.

a. Self-driving car navigation

Although it is too early to see someone reading a newspaper while driving a car, it is a possibility in the future. Sensors and in-vehicle analytics can be used to identify roadblocks to automobile learning. And, using Deep Learning, respond to them properly.

b. Recoloring Black and White Photographs

At this point, computers are required to recognize things. Learn what they should appear like to people as well. Essentially, computers may be trained to return colors. It must also return black-and-white images and movies.

Example: Click Here

c. Precision Medicine

We use Deep Learning to develop medicines. Also, these are genetically tailored to an individual’s genome.

d. Pre-Natal Care

Prenatal Care is the care provided before the birth of a child. To interpret signs, we employ picture recognition and deep learning algorithms. This method is also utilized by researchers for prediction and detection of anomalies in fetus.

e. Weather Prediction and Event Recognition

As a consequence, the computational fluid dynamics codes match neural networks. Other genetic algorithm techniques to detecting cyclone activity are also available.

f. Financial

Typically, prominent technical indicators are used to create buy and sell recommendations. This is true for individual stocks as well as stock portfolios.

g. Automatic Machine Translation

Deep Learning has produced outstanding achievements in the following areas:

Text Translation by Machine
Image Translation via Machine

Let’s end there for now. We hope you found our explanation helpful.

Conclusion

We have looked into Deep Learning in this article. We also investigated Deep Learning applications and use cases. We hope this blog helps you relate to the notion of Deep Learning in real life. Furthermore, if you have any questions, please leave them in the comments area.