The Basics of Machine Learning and How it Differs from Deep Learning
Machine learning and deep learning are two of the most widely used terms in the artificial intelligence industry. Despite their similarities, they have some significant differences that set them apart. In this article, we will discuss the basics of machine learning and deep learning and explore how they differ from one another.
Introduction
Machine learning is an application of artificial intelligence that provides systems with the ability to learn and improve from experience without being explicitly programmed. In contrast, deep learning is a subset of machine learning that uses neural networks to simulate the functionality of the human brain. Both machine learning and deep learning have been applied in a wide range of industries, including healthcare, finance, and marketing.
The Basics of Machine Learning
Machine learning algorithms can be divided into three major categories: supervised learning, unsupervised learning, and reinforcement learning.
Supervised Learning: In supervised learning, the algorithm is trained using labeled data, where the input data is associated with the correct output. The algorithm learns to make predictions by comparing its predicted output with the actual output. Classification and regression problems are examples of supervised learning.
Unsupervised Learning: In unsupervised learning, the algorithm is trained using unlabeled data, where there is no explicit feedback to the system. The algorithm attempts to find patterns in the input data and discover relationships between the variables. Clustering and dimensionality reduction problems are examples of unsupervised learning.
Reinforcement Learning: Reinforcement learning involves an agent that learns from its environment by receiving feedback in the form of rewards or punishments. The agent learns to take actions that increase its chances of getting rewards and avoid punishments.
The Basics of Deep Learning
Deep learning is a subset of machine learning that uses neural networks to simulate the functionality of the human brain. Deep learning models consist of multiple layers of interconnected nodes that process and transform the input data. The output of the last layer is the final prediction or decision.
Deep learning algorithms can be divided into two major categories: convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
Convolutional Neural Networks: CNNs are used for image and video recognition, speech recognition, and natural language processing. CNNs identify patterns in the input data by using filters that slide over the data and extract features. The extracted features are then used to make predictions.
Recurrent Neural Networks: RNNs are used for sequence modelling, such as language translation and speech recognition. RNNs use feedback connections that allow the output of one time step to be fed back into the network as input to the next time step. This feedback loop allows the network to learn from previous predictions and use that information to make new predictions.
How Machine Learning Differs from Deep Learning
Machine learning and deep learning differ in several ways:
Data Requirements: Machine learning algorithms require labeled data for training, while deep learning algorithms can learn from both labeled and unlabeled data.
Hardware Requirements: Deep learning algorithms require more computing power and memory than machine learning algorithms because of the complexity of the neural networks.
Parameters: Machine learning algorithms have a fixed number of parameters that need to be manually tuned, while deep learning algorithms have many parameters that are automatically adjusted during training.
Applications: Machine learning is best suited for problems with structured data, while deep learning is best suited for problems with unstructured data, such as images, audio, and text.
Conclusion
In summary, machine learning is an application of artificial intelligence that provides systems with the ability to learn and improve from experience without being explicitly programmed. Deep learning is a subset of machine learning that uses neural networks to simulate the functionality of the human brain. The major differences between machine learning and deep learning include data requirements, hardware requirements, parameters, and applications. Understanding these differences is crucial for businesses seeking to benefit from artificial intelligence.
