Introduction
In this section, we will explore the fundamentals of machine learning and deep learning, two key areas of artificial intelligence. We will discuss their differences, applications, and the underlying concepts that drive these technologies.
Machine Learning
Machine learning is a subset of artificial intelligence that focuses on developing algorithms that allow computers to learn from and make predictions based on data.
Deep learning and Machine Learning are different from each other based on how they extract features from the data. Machine learning models often require manual feature engineering, while deep learning models can automatically learn features from the data.
Deep Learning
Deep learning is a subset of machine learning that uses neural networks with many layers (hence "deep") to model complex patterns in data. It has been particularly successful in areas such as computer vision, natural language processing, and speech recognition.
Deep learning models perform scales well with increasing data and computational power, making them suitable for tasks that require learning from large datasets. Older learning methods often struggle with high-dimensional data, while deep learning models can automatically learn hierarchical representations of the data, enabling them to capture intricate patterns and relationships.
Future Work:
- Talk about mcculloch-pitts paper.