This book begins by grounding readers in the fundamentals of information retrieval (IR), tracing its evolution from traditional models to modern, neural-based approaches. By examining the inadequacies and constraints of classical models like TF-IDF and BM25, it becomes clear why there's a need to transition to more sophisticated methods that better harness the power of abundant data and computational capacity.

The core of the book delves into deep learning architectures like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, and advanced attention mechanisms employed in neural IR. Each concept is introduced with clarity, supplemented with code examples and real-world applications to enhance understanding. Furthermore, the book evaluates how these neural models perform in comparison to traditional systems across various metrics and datasets.

The latter sections focus on practical implementations, detailing how to build scalable neural IR systems capable of handling web-scale data. Attention is also given to issues like system latency, interpretability of results, and strategies for integrating neural IR with state-of-the-art machine learning pipelines.

• Understand the transition from traditional to neural information retrieval and the driving factors behind it.
• Gain insights into advanced neural architectures and their unique strengths in capturing semantic meaning.
• Learn the intricacies of training, evaluating, and deploying neural IR models in a production environment.
• Explore real-world applications and the transformative impact of neural IR on fields such as e-commerce, healthcare, and media.

"In the era of neural networks, information retrieval is no longer about matching keywords to documents but understanding the intent behind those words."

"The leap from traditional to neural information retrieval is akin to stepping from a library into a conversation with humanity's collective intelligence."