Difference between Machine Learning and Deep Learning
Computers may now learn without specific programming, and this is possible only through the branch of study known as machine learning. Machine learning is also referred to as the most interesting technology which is used widely in this contemporary world. Machine learning consists of the ability to learn when we can take the example, as the name suggests, which makes it more like humans. Also that there are probably a lot many locations that are currently using machine learning on a regular basis.
Advantages of Machine Learning
The advantages of machine learning are:
i) It handles large and complex data.
ii) It improves both reliability and accuracy.
iii) It has a good adaptability feature.
iv) It is scalable.
v) It helps in the detection of fraud and also helps in cybersecurity.
vi) It is used in medical healthcare and diagnostics.
vii) It helps in processing and translation of natural languages.
viii) It is repetitive and automotive.
Disadvantages of Machine Learning
The disadvantages are:
i) It is data dependent which means that learning models are dependent on data for the training.
ii) It lacks transparency as there are some networks that are complex and difficult to understand.
iii) It needs a large or huge amount of data.
iv) It has limited data patterns.
v) It is totally dependent on humans.
Features of Machine Learning
Some of the important features of machine learning are written below:
1.Data is the driving force behind machine learning. Huge quantities of data produced daily by organizations. Therefore, organizations make better decisions as a result of important connections in data.
2. The machine can automatically improve by using past information to learn.
3. It generates different data patterns from the provided dataset.
4. Branding is essential for large organizations because it makes it simpler to target a consumer base that can relate to them.
5. Machine learning algorithms can find broad patterns in data that can be used for examining brand-new, unknown data. In spite of the fact that the data which is being used to train the model may not be directly applicable to the task together, yet they are still useful for predicting the upcoming events.
6. The algorithms used in machine learning are created to learn and adjust when new data becomes available. And as a result, more data is made accessible such that it improves their performance over time, and becomes more accurate and effective.
Deep learning
A subset of machine learning called "deep learning" uses artificial neural networks as its basis. It has the capacity to discover complex patterns and connections inside data. In deep learning, it is not everything which is needs to be specifically programmed. Deep learning has increased throughout this recent years which as a result, provides easy availability of vast amounts of information and also in the advancements in processing speeds. Due to this fact, it is based on deep neural networks (DNNs) which is also referred to as artificial neural networks (ANNs). These neural networks were developed in order to learn from the huge amount of data. These were also designed after the structure and operation of real brain neurons.
The usage of deep neural networks consists of numerous layers of nodes which are linked with each other, and it is therefore said to be the main aspect of deep learning. By identifying hierarchical patterns and features in the data, these networks are able to learn complicated representations of the data. Without the need of manual feature engineering, deep learning algorithms can learn from data and in turn get better.
Advantages of Deep Learning
The advantages of deep learning are given below:
1. High precision: Deep Learning algorithms are performed at the highest level and that too in a variety of tasks, which includes image identification and natural language processing.
2. Automated feature engineering: Deep Learning systems can automatically find and learn appropriate features from data, without the need for manual feature engineering.
3. Scalability: Deep Learning models consist of information from huge amounts of data and scale to address big and complicated datasets.
4. Flexibility: Deep Learning models are capable of processing a wide range of tasks and various types of data, which include images, text, and speech.
5. Continuous performance improvement: Deep Learning models can boost their efficiency as new data becomes available.
Disadvantages of Deep Learning
Data availability: It requires large amounts of data to learn from. For using deep learning it’s a big concern to gather as much data for training.
Computational Resources: For training the deep learning model, it is computationally expensive because it requires specialized hardware like GPUs and TPUs.
Time-consuming: While working on sequential data depending on the computational resource it can take very large even in days or months.
Interpretability: Deep learning models are said to be complex, it works like a black box. It is very difficult to interpret the result.
Overfitting: when the model is trained certain time, such that it becomes too specialized for the training data, which leads to overfitting and poor performance on new data.
Difference Between Machine Learning and Deep Learning
| MACHINE LEARNING | DEEP LEARNING |
| -In machine learning even though it requires a vast amount of data, it can easily perform functions with fewer data. | -In case for Deep Learning algorithms to perform well, a lot of data must be provided as they are heavily dependent on it. |
| -The standard ML methodology divides a problem into smaller parts and solves each of them separately before giving the final answer. | -A deep learning model deals with problems differently than a typical Machine Learning model since it takes input for a particular problem and produces the result. As a result, it uses an end-to-end strategy. |
| -In machine learning structured data is needed for the models. | - Whereas in Deep Learning models, it depends on an artificial neural network for its levels, as it can process both structured and unstructured data. |
| -There are many thousands of data points in machine learning. | -Millions of data points comprise big data. |
| -Simple linear models are used in machine learning algorithms, as well as the advanced ones too such as decision trees and random forests. | -On the other hand, deep learning techniques are only based on artificial neural networks which include several layers and nodes. |
| -Machine learning models can be used to resolve simple or complex problems. | -Complex problems are best solved with deep learning models. |
| -The efficiency of machine learning systems may be restricted considering their simplicity of implementation and operation. | -Deep learning algorithms need more setup time, but they can produce results right away,(but the quality is likely to improve with time as more data becomes accessible). |
| - In machine learning the time duration required to train is completed in a few seconds or so to say a few hours. | -Whereas in a deep learning systems it takes a long time to train since it needs such large amounts of data, contains so many parameters, and also uses complex computations. Deep learning takes a few hours to a few weeks for training. |
Conclusion
In conclusion, we can say that the key difference or the major difference between both machine learning and Deep learning is that of time, its complexity, and also the depth of it. As we already know that machine learning is generally dependent on the algorithm which is used for both decision-making and for future forecasting, and this happens only because data has its relationship. This algorithm mainly needs human for its functioning or we can say it is human-dependent.
Whereas, Deep learning it is defined as a branch of machine learning that deals primarily with deep neural networks. This type of network is made up of many layers and of interconnected nodes (neurons) which are used to train and learn for hierarchical representations of the data. Deep learning models have the capacity to develop their own features directly from raw data, removing or neglecting the requirement of feature engineering.
In real-world applications, the difference between machine learning and deep learning depends on the particular problem faced and this comprises of the data at present, and the amount of computing power available, further with the need for accessibility, and the knowledge on board. Both machine and deep learning comprise of advantages and disadvantages, so it is therefore important for researchers and practitioners to choose carefully which best meets their requirements and constraints.
Therefore it is said that both machine learning and deep learning are said to be under the general heading of artificial intelligence.