Artificial Intelligence, Machine Learning , and Deep Learning have always been confusing buzzwords, which are often used interchangeably. It is important to study various AI branches to study within. This will help us in choosing the right framework to solve a real-world problem. Deep Learning and ML are the subfields of AI.
Let us understand and differentiate these concepts under this topic.
AI: AI is a big picture and an umbrella term that develops machines which can accomplish a task that requires human intelligence. AI does not imply learning. AI falls into one of the three stages, which we have discussed in the preceding topics about AI concepts and types of AI based on their capabilities.
Machine Learning: This field is a subset of AI which deals with making the machines learn from past data without being explicitly programmed. But how machines can learn? It can learn just the way human learns. Humans can learn through communication, past experiences, analyzing the situation, or decision-making.
A machine can learn the same way with the help of data and algorithms. The algorithm finds out the hidden patterns in the data and helps us to make future predictions or infer knowledge from the data. With more and more data you give to the model, it further gets improved, leading to accuracy. ML automates repetitive learning. ML is broadly categorized into three types:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
Deep Learning (DL): It is a subset of ML, which mimics like human brain/ neurons while processing the data such as object recognition, language translation, decision-making, and so on. Geoffrey Hinton, with his fellow researchers, has triggered the success of Deep Learning. Just like neurons are the basic unit of the nervous system in the human brain, DL uses neural network architecture to solve the given problem without human intervention. The input data passes through multiple layers and classifies the information. It requires a huge amount of data, unlike ML, for learning. It solves complex machine learning problems. An example is a self-driving car that uses Deep Learning to detect any obstacle that comes while driving a car.
Hope this was helpful.
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