Deep Learning for NLP
( 30 Modules )

Module #1
Introduction to Deep Learning for NLP
Overview of deep learning and its applications in NLP, course objectives, and prerequisites

Module #2
Mathematical Preliminaries
Review of linear algebra, calculus, and probability theory necessary for deep learning

Module #3
Introduction to Python and TensorFlow/Keras
Basics of Python programming and introduction to TensorFlow/Keras deep learning frameworks

Module #4
Word Embeddings
Introduction to word embeddings (Word2Vec, GloVe), their importance in NLP, and implementation in TensorFlow/Keras

Module #5
Recurrent Neural Networks (RNNs)
Introduction to RNNs, architecture, and implementation in TensorFlow/Keras

Module #6
Long Short-Term Memory (LSTM) Networks
Introduction to LSTMs, architecture, and implementation in TensorFlow/Keras

Module #7
Gated Recurrent Units (GRUs)
Introduction to GRUs, architecture, and implementation in TensorFlow/Keras

Module #8
Sequence-to-Sequence Models
Introduction to sequence-to-sequence models, architecture, and implementation in TensorFlow/Keras

Module #9
Natural Language Processing (NLP) Tasks
Overview of common NLP tasks (sentiment analysis, machine translation, named entity recognition)

Module #10
Text Preprocessing
Importance of text preprocessing, tokenization, stemming, lemmatization, and normalization

Module #11
Sentiment Analysis
Introduction to sentiment analysis, implementation using TensorFlow/Keras, and evaluation metrics

Module #12
Named Entity Recognition (NER)
Introduction to NER, implementation using TensorFlow/Keras, and evaluation metrics

Module #13
Machine Translation
Introduction to machine translation, implementation using TensorFlow/Keras, and evaluation metrics

Module #14
Convolutional Neural Networks (CNNs) for NLP
Introduction to CNNs, architecture, and implementation in TensorFlow/Keras for NLP tasks

Module #15
Attention Mechanisms
Introduction to attention mechanisms, implementation in TensorFlow/Keras, and applications in NLP

Module #16
Transformers and BERT
Introduction to transformers, BERT, and implementation in TensorFlow/Keras for NLP tasks

Module #17
Deep Learning for Question Answering
Introduction to question answering, implementation using TensorFlow/Keras, and evaluation metrics

Module #18
Deep Learning for Text Classification
Introduction to text classification, implementation using TensorFlow/Keras, and evaluation metrics

Module #19
Deep Learning for Language Modeling
Introduction to language modeling, implementation using TensorFlow/Keras, and evaluation metrics

Module #20
Advanced Topics in Deep Learning for NLP
Overview of advanced topics in deep learning for NLP (multi-task learning, transfer learning, etc.)

Module #21
Practical Applications of Deep Learning for NLP
Real-world applications of deep learning for NLP (chatbots, speech recognition, etc.)

Module #22
Deep Learning for NLP with Limited Data
Techniques for deep learning with limited data (data augmentation, transfer learning, etc.)

Module #23
Explainability and Interpretability in Deep Learning for NLP
Importance of explainability and interpretability in deep learning for NLP

Module #24
Ethical Considerations in Deep Learning for NLP
Ethical considerations in deep learning for NLP (bias, fairness, etc.)

Module #25
Project Development and Deployment
Guidelines for developing and deploying deep learning NLP projects

Module #26
Case Studies in Deep Learning for NLP
Real-world case studies of deep learning for NLP applications

Module #27
Panel Discussion:Industry Experts in Deep Learning for NLP
Panel discussion with industry experts in deep learning for NLP

Module #28
Future of Deep Learning in NLP
Future directions and advances in deep learning for NLP

Module #29
Course Wrap-up and Next Steps
Review of course material, next steps, and resources for further learning

Module #30
Course Wrap-Up & Conclusion
Planning next steps in Deep Learning for NLP career

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Deep Learning for NLP ( 30 Modules )

Deep Learning for NLP
( 30 Modules )