One of the key areas for developing a profession in artificial intelligence and technology in general is deep learning. The significance of deep learning algorithms and machine learning technology is growing along with technology. No one can deny the influence of artificial intelligence, but those who are interested sometimes lack direction and are unclear about the training and skills they should pursue. One can take up effective AI Learning courses to gain a better understanding of deep learning algorithms.
Read on to navigate the deep learning technology landscape and understand the intricate distinctions between deep learning and machine learning algorithms.
What are Deep Learning Algorithms?
Deep learning is a vital branch and a subfield of machine learning that works best with neural networks and artificial intelligence. With the help of machine learning algorithms, one can solve complex problems by incorporating neural networks. Neural networks consist of layers of interconnected nodes that process and transform user input into modeled data which is similar to the operation and structure of a human brain.
Deep learning algorithms help to easily represent complex data by locating certain features and hierarchical patterns in the data. Deep learning algorithms can dynamically learn from data and get better by themselves without explicit feature construction. Deep learning is extensively used in multiple areas such as speech recognition, natural language processing, image recognition, recommendation systems and so on. One can take up AI ML training to better understand the concepts and functions of deep learning.
What is Machine Learning?
Machine learning is a branch of artificial intelligence that uses a variety of algorithms and methods to gather the data required to predict future trends. Machine learning experts understand data patterns and then perform a statistical analysis using specific software applications. This enables them to operate in accordance with various data models.
Systems may learn from user activity which eliminates the need for explicit programming. Algorithms that use machine learning can continuously improve forecasts by learning from prior results and predictions.
How is Deep Learning Different From Machine Learning
Deep learning and machine learning are closely related to each other and operate in a very similar way. Machine learning evolved through artificial intelligence but deep learning algorithms are an evolution of machine learning. Individuals often get confused about the two concepts because of their similar nature.
However, deep learning is only a subset of machine learning. Machine learning is an entire domain of which deep learning is a part. To understand the intricate differences between the two, one can take up an AI for executives course that will allow them to advance in their careers with clear-cut concepts.
The major differences between deep learning and machine learning algorithms are stated as under:
| Basis | Deep Learning | Machine Learning |
| Scope | Deep learning is a subset of machine learning and is comparatively a narrow concept. | Machine learning is the superset of which deep learning is a part. Machine learning is a wider concept. |
| Type of data | The type of data and its representation are quite different in deep learning as it uses neural networks (CNN) to work with different data structures. | Machine learning uses structured data only and hence the data represented here is different from that of deep learning. |
| Output value | Deep learning algorithms may have outputs in the form of free elements. It can be anything starting from numeric values, speech, sound, text etc. | Machine learning only bears output in the form of numeric values. |
| Uses what? | Deep learning algorithms use neural networks that work just like a human brain to process, intercept and transform data. | Machine learning algorithms use structured data or numerous automated algorithms that help to represent, prepare and assume future actions based on the data. |
| Importance | Deep learning algorithms are significant in solving complex problems or machine learning issues. | Machine learning is important for technological advancement in artificial intelligence and to discover new things |
| How is it performed? | Deep learning algorithms are performed with the help of a dedicated Graphics Processing Unit (GPU). | Training in machine learning is carried out with the help of a Central Processing Unit (CPU). |
| Degree of human intervention | Deep learning requires less human intervention as it can automatically run and model data. | To obtain results using machine learning algorithms, more human intervention is required. |
| Setting up | Deep learning is much more difficult and complex to set up when compared to machine learning. | Machine learning is comparatively easier to set up and is less complex. |
| Requirement of feature engineering | Deep learning algorithms do not require explicit feature engineering because neural networks can detect critical features. | Machine learning algorithms require individuals to perform explicit feature engineering. |
| Degree of accuracy | The output of deep learning algorithms is much more accurate when compared to the output of machine learning algorithms. | Machine learning algorithms require much more computational capital to execute complex tasks and generate results. As they are difficult to train, the results may be a little inaccurate. |
| Use cases | Deep learning algorithms are generally used for solving complex tasks such as image recognition and processing, speech recognition, autonomous systems and natural language processing. | Machine learning algorithms are used in a variety of cases such as in regression, classification and clustering of data |
| Data points | Deep learning algorithms are based on big data which consists of millions of data points. Its model training requires more time as it contains very large data points. | Machine learning algorithms consist of comparatively fewer data points (thousands). Hence, its model training requires less time because of its smaller size. |
Conclusion
Nowadays, deep learning algorithms are used in a wide range of industries. Deep learning is capable of producing exact results and precise forecasts. It pulls useful data that provides useful insights into the data sets and works well with unstructured data. Deep learning has a wide range of talents that are in demand, which keeps its attraction high. You may take AI learning courses to get a grasp on the concepts of deep learning and ML.
If you want to become a deep learning expert, you may consider signing up for the IIM Lucknow Executive Programme In AI For Business by Imarticus. Artificial neural networks have improved thanks to deep learning, which is regularly in contact with AI and ML. Be at the forefront of an AI revolution and get lucrative job opportunities that offer handsome salaries.
