Machine learning (ML) is a subset of AI that enables computers to learn from data without explicit programming. By analyzing data, ML algorithms identify patterns, make predictions, and improve over time.

Deep learning is a specialized type of machine learning that uses multi-layered neural networks (hence 'deep') to model complex patterns in large datasets. Deep learning is particularly effective for tasks such as image recognition, natural language processing (NLP), and other data-intensive applications.

In short, ML focuses on finding patterns and making predictions from data, while deep learning excels at modeling complex data relationships through layered neural networks.

Learn more about machine learning vs. deep learning.

Neural networks are the foundation of deep learning. Inspired by the human brain, these models process data in multiple steps through interconnected nodes, or ‘neurons,’ organized in layers. The more layers a network has, the more effectively it can recognize complex patterns and make sophisticated decisions.

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Convolutional neural networks (CNNs) are a type of deep learning architecture used primarily for image processing. CNNs scan images in small sections, detecting patterns like edges, textures, or shapes. By stacking multiple layers, CNNs can recognize complex visual elements, which make these neural networks ideal for tasks like object detection and image classification.

Learn more about convolutional neural networks.

Recurrent neural networks (RNNs) are a type of deep learning architecture designed to process sequential data, where order is essential, such as in language. RNNs retain information from previous steps, allowing them to simulate memory and making them suitable for tasks like language modeling, speech recognition, and time-series predictions.

Learn more about recurrent neural networks.

Transformer models are a type of deep learning architecture designed to handle sequential data, making them particularly effective for natural language processing (NLP) tasks like translation, text generation, and sentiment analysis. Unlike traditional architectures like recurrent neural networks (RNNs), which process sequences one element at a time, transformers use self-attention to analyze entire sequences simultaneously. This approach enables them to capture relationships between distant words or data points efficiently, even over long sequences.


Transformers' ability to process multiple inputs in parallel makes them fast and scalable, especially for large datasets. This efficiency has made transformers the foundation for many advanced language models, such as GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers), which drive a range of modern NLP applications.

Learn more about transformer models.