Natural Language Processing with TensorFlow equips participants with the skills to develop sophisticated language models and applications using TensorFlow. This course emphasizes hands-on project work, enabling learners to apply theoretical concepts in practical scenarios. Participants will engage with real-world datasets and utilize TensorFlow’s powerful capabilities to build, train, and evaluate NLP models. The interactive nature of the course fosters collaboration and encourages participants to publish their findings in Cademix Magazine, enhancing their professional visibility.
The curriculum covers a comprehensive range of topics, beginning with fundamental NLP concepts and progressing to advanced techniques. Participants will gain insights into text preprocessing, model architecture selection, and the deployment of NLP solutions. By the end of the course, learners will have developed a final project that showcases their understanding and application of Natural Language Processing with TensorFlow, preparing them for roles in data science, AI development, and language technology.
Introduction to Natural Language Processing and TensorFlow
Text preprocessing techniques (tokenization, stemming, lemmatization)
Understanding word embeddings and their applications
Building recurrent neural networks (RNNs) for sequence prediction
Implementing long short-term memory (LSTM) networks for NLP tasks
Exploring transformer models and attention mechanisms
Sentiment analysis using TensorFlow
Named entity recognition (NER) with deep learning
Text generation and language modeling
Final project: Developing a complete NLP application using TensorFlow