Abstract : This chapter embarks on a journey through the dynamic landscapes of Deep Learning and Natural Language Processing (NLP) with a focus on the TensorFlow and Keras frameworks, as well as spaCy and NLTK libraries. In this chapter, we demystify the world of deep learning, exploring neural networks, activation functions, and model training. We introduce TensorFlow, a powerful open-source framework, and Keras, a user-friendly high-level API that simplifies deep neural network creation. Case studies illuminate deep learning's real-world impact, spanning image classification to healthcare applications. We conclude by delving into transfer learning and pre-trained models, essential tools for modern deep learning. This chapter also dives into NLP, emphasizing its pivotal role in today's technology landscape. We introduce spaCy and NLTK, two Python libraries that equip you with versatile NLP tools. Sentiment analysis, text classification, and Named Entity Recognition are highlighted, showcasing their applications across various domains. By the end, you'll have a robust foundation in both deep learning and NLP, equipped to tackle real-world challenges and opportunities in these cutting-edge fields.
Keywords : Deep Learning, Natural Language Processing (NLP), TensorFlow, Keras, spaCy, NLTK
Cite : Gouri, M. H., & Nafees, M. (2023). Harnessing Deep Learning And Natural Language Processing (NLP) (1st ed., p. 9). Noble Science Press. https://doi.org/10.52458/9789388996747.nsp2023.eb.ch-02
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