So, let’s start Deep Learning Tutorial.
Any Deep neural network will consist of three types of layers:
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 perceptrons in each layer will depend on the use-case you are trying to solve.
3. Output Layers
It provides the desired output.
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To feed a computer system with a lot of data we use deep learning. The system then uses these data to make a decision about other data. This data feeding takes place through neural networks.
Moreover, Deep Learning is crucial because it focuses on developing these networks. As a result, they are known as Deep Neural Networks.
3.Deep Learning Tutorial – What is Neural Networks?
- It is a beautiful biologically programming paradigm. Also, enables a computer to learn from observational data.
- Also, it provides the best solutions to many problems. That are image recognition, speech recognition, and natural language processing.
4. Deep Learning Tutorial – Use Case
Deep Learning Tutorial – Use Case
Here, in this use case, we are passing the high dimensional data to the input layer.
- To match the dimensionality of the input data, the input layer will be needed. This contains multiple sub-layers of perceptions so that it can consume the entire input.
- The input layer will contain patterns which were received from the output. Also, it has the ability to identify the edges of the images based on the contrast levels
- This output will be fed to the hidden layer 1. And in this layer, it will be able to identify various face features like eyes, nose, ears etc.
- Now, this will be fed to the hidden layer 2 where it will able to form the entire faces. Then, the output of layer 2 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.
5. Deep Learning Tutorial – Applications
Let’s discuss some Deep Learning Applications.
a. Navigation of Self-driving cars
Although it is too early to catch someone reading a newspaper while driving cars are in the future. To recognize obstacles to car learning, we can use sensors and inboard analytics. And also react to them appropriately using Deep Learning.
b. Recolouring Black and White Images
At this time, computers are necessary to recognize objects. Also, learn what they should look like to humans. Basically, computers can be used to taught to return colors. Also, it needs to return black & white pictures and videos.
Won’t it be amazing to see Devdas (1955) in color?
c. Predicting the outcome of Legal Proceedings
British and American researchers had developed a system. They used that system to predict court’s decision.
d. Precision Medicine
We use Deep Learning to develop medicines. Also, these are genetically tailored to an individual’s genome.
e. Automated analysis and Reporting
We are too much thankful for deep learning techniques. As we can see that the systems can now analyze data. Also, report insights from its natural soundings and human language.
f. Pre-Natal Care
We use image recognition and deep learning techniques to interpret signs. Also, this technique is used by UK and Australian researchers. Also, guide pre-operative strategies.
g. Weather Forecasting and Event Detection
As a result, the computational fluid dynamics codes are matching with neural networks. Also, other genetic algorithm approaches to detect cyclone activity.
Usually, we use popular technical indicators to generate buy and sell signals. That is for each stock and for portfolios of stocks.
i. Automatic Machine Translation
Deep Learning has been achieving amazing results in the following area as:
- Automatic Translation of Text
- Automatic Translation of Images
So, this was all about Deep Learning Tutorial. Hope you like our explanation.
As a result, we have studied Deep Learning Tutorial and finally came to conclusion. Also, we have studied Deep Learning applications and use case. I hope this blog will help you to relate in real life with the concept of Deep Learning. Furthermore, if you feel any query, feel free to ask in the comment section.