Introduction:
In today’s world, technology is rapidly growing and has started providing intelligent solutions to mankind’s day-to-day problems. Understanding the current technology trends, we are in a modern era where Intelligence is built into the machines so that they can take decisions and perform actions similar to that of a human. This is no more a science fiction or a myth, using Artificial Intelligence modern machines are being built across the world.
So what does Artificial Intelligence really mean?
In layman’s terms, Artificial Intelligence can be described as a program that can think by itself and self-learn.
Now let us understand more about the technologies and concepts that aid the usage of Artificial Intelligence and its importance. Out of all the core concepts like Machine Learning and Deep Learning plays a vital role in a successful implementation.
What is Machine Learning?
- Machine learning is one of the data analytics techniques which primarily focuses on understanding the data sets and deriving results from the large data sets.
- The Machine learning algorithms don’t have a pre-coded equation but it focuses on understanding the data sets and the model evolves based on the experiences.
Check out Machine Learning Training.
How does Machine Learning Work?
Machine Learning primarily works on three techniques :
- Supervised Learning
- Unsupervised learning
- Reinforcement learning
What is Supervised learning?
- As the name suggests, the learning process is carried out under strict observation with appropriate inputs. In this technique, the Machine Learning model is fed with a rightful set of data so that it can understand and derive expected results. Once this process is completed, new datasets are fed to the Machine Learning model and it is expected to work in the same pattern.
- This technique is good to use when we know enough data elements. This will help us to predict the outcomes.
What is Unsupervised learning?
- This technique is opposite to supervised learning.
- In the Unsupervised learning process, the machine learning model is fed with datasets so that it can detect the different patterns and draw conclusions on its own.
- Generally, an unlabelled data set is used in these scenarios.
What is Reinforcement learning?
- Reinforcement learning is similar to that of supervised learning but the outputs are varied.
- In this process, the decisions are made sequentially
- The decisions are made at every stage, i.e. output of the current step is based on its inputs.
Now we have learned about the basics of Machine learning and its techniques let us focus and read more on Deep Learning Training here.
What is Deep Learning?
Deep learning is part of a machine learning technique that primarily focuses on training the models with huge datasets (i.e. labeled datasets) with the help of neural network architectures.
How does Deep Learning work?
- Deep learning works on neural network architectures.
- The layers are stacked upon one another forming a network. The learning phase is achieved through this network.
- These networks are trained with huge volumes of data sets so that definitive learnings are derived out of the data itself.
- As discussed earlier, a network is a combination of different layers stacked together with a neural network that consists of three main layers, they are:
- Input Layer
- Hidden Layers (this can be many)
- Output Layer
The data is passed through the Input layer and the calculations are carried out in Hidden Layers and conclusions/findings are derived at the output layer.
Conventional Neural Networks or often represented as CNN is the most popular technique used in deep learning models.
Differences between Machine Learning and Deep Learning
| Machine Learning | Deep Learning |
| Machine Learning is a supersize of Deep learning | Deep Learning is a subset of Machine Learning. The inputs from Deep Learning help the machine learning models to evolve. |
| Evolution of Machine Learning leads to Artificial Intelligence. | The evolution of Deep learning leads to the maturity of the Machine Learning model. |
| Data points obtained by Machine learning are comparatively less. | Data points obtained by Deep Learning are more. The models use huge data sets, i.e. Big Data |
| The output of a Machine Learning model is generally in the form of a conventional score ( numerical values) | The output of Deep learning models can be anything from numeric values to text etc. |
| Uses various algorithms which derive model functions based on the data sets. The model further helps in predicting the results. | Uses neural networks to process the data and derive results and observations |
| Machine learning is used to predict outcomes. | Deep learning is used to solve hypercomplex issues. |
| Rely on data as an input | Rely on data as an input. |
| Features are extracted from the data sets and data analysts conclude the classifications | Features are extracted from the data sets and classification of data happens within the neural networks |
| Machine Learning models can still work on smaller data sets | Deep learning models require huge datasets |
| The execution time or training time of a machine learning model is comparatively low when compared to Deep learning models | Deep learning models required higher execution time to train as they deal with huge data sets |
| No specific hardware requirements are necessary as machine learning models can still work on low-end devices | Higher configuration machines are needed to execute deep learning models |
| Primarily works with structured data sets | Works with both structure and unstructured data sets |
Use this image to showcase the difference between Machine Learning and Deep Learning
Real-time use cases of Machine Learning:
The below table gives information on real-time usage of Machine learning across different business sectors:
| Business Area | Implementation of Machine Learning |
|
Used for Patient Diagnosis |
|
Recommendation systems is the best example |
|
Inventory optimization and predictions |
|
Demand and Supply Predictions |
|
Fraud Detection, AML |
Real-time use cases of Deep Learning:
The below table gives information on real-time usage of Deep learning across different business sectors:
| Business Area | Implementation of Deep Learning |
|
Self driving cars |
|
Understands the human context and fulfilling the needs |
|
Automatic translations |
|
Enhancing customer satisfaction |
Conclusion
In a nutshell, deep learning is a kind of machine learning but has extra capabilities and also has a different approach towards analyzing and deriving results. Based on the problem area and amount of the data sets one can choose appropriate models.
Author Bio:
I’m Sudheer Kuragayala, an enthusiastic Digital Marketer and content writer working at Mindmajix.com. I wrote articles on trending IT-related topics such as Artificial intelligence, Cloud Technologies, Apigee Online Training, Business Tools, and Softwares. You can reach me on Linkedin: Sudheer Kuragayala.
