- Deep learning is a technique of Machine Learning wherein Machine learning is a part of Artificial Intelligence.
- Deep learning is based on neural networks. The learning process is very complicated in Deep learning due to the structure of artificial neural networks. It consists of multiple inputs, outputs, and hidden layers. Every layer contains units that transform the input data into information that the next layer can use for a certain predictive task. Due to this structure, a machine can learn through its own data processing.
- How Machine Learning works in Artificial Intelligence?
- Add data into an algorithm. Any additional information can be fed in this step.
- Data is used to train the required model.
- Test and deploy the model.
- It consumes the deployed model to do an automated predictive task.
Analytical Knowledge between Deep Learning and Machine Learning:
- The number of data points: Machine learning uses small data to make few predictions wherein Deep Learning uses large amounts of data to make predictions.
- Featurization process: Machine Learning requires features to be accurately identified and created by the user. Deep Learning requires high-level features from data and creates new features by itself.
- Hardware dependencies: Machine Learning can work on low-end machines. It requires low computational power wherein Deep learning works on high-ending machines and requires high computation power.
- Execution time: Machine learning takes comparatively less time with Deep Learning.
- Output: In Machine Learning output comes in numeric values wherein Deep Learning output comes in various formats like text, sound, etc.
- Learning approach: Machine Learning works by dividing the learning process into smaller steps. Then it combines the results from each step into one output.
The main difference between deep learning and machine learning is due to its execution as the size of data increases. Algorithms in Deep learning need a large amount of data, this is why, when the data is small those algorithms don’t perform that well. On the other hand, algorithms in machine learning with their high-quality principles win in this situation as they perform well in the cases of small data.