Getting started with Deep Learning
Deep learning is an extremely popular topic today and people with knowledge in the same field are highly in demand.
In this blog, we will cover what deep learning is and what are its applications. In addition, we will gain an in-depth knowledge of its works and other topics related to the same.
List of topics to be covered
Check out this video to gain in-depth knowledge about deep learning concepts.
We’ve been talking about how amazing deep learning is. But wait! What is this term?
Let’s jump into our first topic and understand deep learning definition.
What is Deep Learning?
Deep Learning is the subset of ML or Machine Learning. Machine Learning, whereas, is a subset of Artificial Intelligence.
The algorithms in DL are trained in a way that they are expected to produce outcomes similar to what a human brain would do.
In deep learning, features or factors on which the attributes are to be classified, are decided by the artificial neural networks, without any human interference.
So, let us first understand what is meant by the terms we used above:
Artificial Intelligence: A technique through which machines are trained to mimic human-like behavior.
Machine Learning: A technique in which data is used to run specific algorithms. These algorithms are used to train machines to achieve desired outcomes.
Deep Learning: It is a type of ML. DL uses an artificial neural network, which is inspired by the structure of the human brain.
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Let us now dive into understanding the deep learning framework and how it works.
How Deep Learning works?
Now it’s time to learn the working of deep learning and here are the following points:-
- The Deep Learning technique comprises a multi-layered network, known as artificial neural networks.
- Neurons are the core entity of neural networks. This is where information processing of the network takes place.
- Every neuron has a Bias. Bias is a unique number associated with each neuron.
- A neural network consists of layers. Every network has an input and an output layer. The hidden layers and the number of neurons in each layer have to be decided based on the algorithm and desired outcome.
- The input layer receives the input. The hidden layers perform mathematical computations on the neurons. The output layer predicts the result or the outcome.
- The information in the network is transmitted from one layer to another. This transmission of information takes place through channels.
- Each channel is assigned a unique numerical value which is called its Weight. Hence, it is known as a Weighted Channel.
- Bias is added to the weighted sum of inputs reaching the neuron. This is then applied to a threshold function named the Activation Function.
- The outcome of the Activation Function determines whether a neuron will get activated or not.
- The activated neurons transmit the information to the next layer through channels, up to the second last layer of the neural network.
- The neuron that is activated in the rearmost layer i.e. the output layer, with the highest value, is the one corresponding to the input digit. It determines the output.
To design a well-trained and efficient network, the weights and biases in the neural network have to be continuously adjusted.
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Deep Learning Applications
Almost all industries in the present day make use of deep learning in one or the other way. It has a vast range of applications. Given below are some fascinating deep learning examples:-
Digitalization has many advantages, but with these advantages come the cons. One such disadvantage is Fraud. To overcome this issue of fraud, several organizations have started making use of deep learning concepts.
With the help of machine learning and neural networks, companies collect information, identify patterns in transactions, and discover unusual behaviors.
This way they can detect fraud and eventually take appropriate measures to prevent it.
Almost everyone in the world today knows who google assistant, Siri, and Alexa are. They have slowly but surely gained their position and have become a part of numerous peoples’ daily lives. They can do everything from answering questions to playing songs and controlling smart home devices.
All these virtual assistants, although different, have the same basics. All of them are products of deep learning algorithms.
The primary factor behind autonomous driving is deep learning. DL algorithms are used to model the data, and as a result, judgments are made that are appropriate for the driving situation.
Neural networks help in identifying and navigating between different parts of the road and enable autonomous driving.
Self-driving cars today take into account your destination and map out the fastest, as well as, the most optimal routes.
The healthcare industry has witnessed several notable achievements since deep learning algorithms have been introduced into it.
One prominent application of deep learning in the healthcare sector is the detection and treatment of cancer.
Deep learning principles are applied to analyze MRI, X-Ray, and CT scan to provide detailed reports.
Numerous people contact and converse with customer support agents. Most of them do not even realize that they are talking to bots on the other end.
This is because with the help of deep learning the conversation seems too real to discern the difference.
Are you someone who usually gets machine and deep learning mixed up?
Keep reading to learn how to differentiate between the two.
Deep Learning vs Machine Learning
|DEEP LEARNING||MACHINE LEARNING|
|It is the subset of Machine Learning. Deep learning can handle both structured and unstructured data. ||It is the superset of Deep Learning and a subset of Artificial Intelligence. Machine learning mostly requires structured data. |
|It is used to solve complicated ML problems as it is trained to produce results mimicking the human brain.||It is used to solve simple or semi-complex problems.|
|Unlike machine learning, it uses an end-to-end approach to solve problems.||Machine learning breaks and solves each part to give the final result.|
|Deep learning is more effective as compared to Machine learning.||Although machine learning can easily be set up and run, it is comparatively less efficient than deep learning.|
|It requires a larger volume of data to train a machine.||It requires a relatively lesser amount of data to train the model.|
|Due to massive amounts of data, deep learning algorithms require more time to execute.||It takes less time than deep learning to execute. But it requires a long time to test the model.|
|When compared to machine learning, deep learning requires more stronger and powerful resources or hardware. ||High-end machines are not required for machine learning. It works well even with standard and low-end machines. |
With the rapidly increasing technologies and their applications, it is necessary to understand what the future looks like for them.
So, what does the future hold for deep learning?
Future scope of Deep Learning
Deep learning has a lot of scope in the future in various industries.
- It will help in the advanced digitalization of present manual machines.
- It will help in transforming the current state of organizations by helping them make better decisions.
- It will assist in improving the existing methods of data storage.
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Deep learning has become one of the most crucial parts of technology today. One of the main reasons why it is flourishing so extensively is its capability to provide better and modified data-driven decisions. With its applications and advantages, it is evident that deep learning is a great advancement in technology and that it is here to stay. Machine Learning is applied to almost every area and it is a powerful tool that opens up numerous opportunities. People with Machine Learning Certification have the chance to kick-start their careers in the field of ML.
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