Neural networks are essential in machine learning because they excel at solving complex problems where traditional algorithms struggle. They are capable of learning hierarchical representations from raw data, making them highly effective for unstructured data such as images, text, and audio. Unlike rule-based systems, neural networks can generalize from examples, enabling tasks like image recognition, language translation, and speech processing. Their flexibility and scalability allow them to adapt to a wide range of applications, from predicting stock prices to autonomous driving.
Why do we even need neural networks in machine learning?
- Large Language Models (LLMs) 101
- Retrieval Augmented Generation (RAG) 101
- Evaluating Your RAG Applications: Methods and Metrics
- The Definitive Guide to Building RAG Apps with LlamaIndex
- Natural Language Processing (NLP) Advanced Guide
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