Deep Learning Techniques for NLP
( 25 Modules )

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

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

Module #3
Python Basics for NLP
Introduction to Python libraries and tools for NLP (NLTK, spaCy, pandas)

Module #4
Introduction to TensorFlow and Keras
Setting up and basics of TensorFlow and Keras for deep learning

Module #5
Word Embeddings
Introduction to word embeddings (Word2Vec, GloVe) and their applications

Module #6
Language Modeling
Basic language models and their applications in NLP

Module #7
Recurrent Neural Networks (RNNs)
Introduction to RNNs and their applications in NLP (language modeling, text classification)

Module #8
Long Short-Term Memory (LSTM) Networks
Introduction to LSTMs and their applications in NLP (language modeling, sentiment analysis)

Module #9
Gated Recurrent Units (GRUs)
Introduction to GRUs and their applications in NLP (language modeling, machine translation)

Module #10
Bidirectional RNNs and Encoder-Decoder Models
Introduction to bidirectional RNNs and encoder-decoder models for machine translation and text summarization

Module #11
Convolutional Neural Networks (CNNs) for NLP
Introduction to CNNs and their applications in NLP (text classification, sentiment analysis)

Module #12
Attention Mechanisms
Introduction to attention mechanisms and their applications in NLP (machine translation, question answering)

Module #13
Transformers and BERT
Introduction to transformers and BERT and their applications in NLP (language modeling, question answering)

Module #14
Text Classification with Deep Learning
Applying deep learning to text classification tasks (sentiment analysis, spam detection)

Module #15
Named Entity Recognition (NER) with Deep Learning
Applying deep learning to NER tasks

Module #16
Dependency Parsing with Deep Learning
Applying deep learning to dependency parsing tasks

Module #17
Machine Translation with Deep Learning
Applying deep learning to machine translation tasks

Module #18
Question Answering with Deep Learning
Applying deep learning to question answering tasks

Module #19
Text Generation with Deep Learning
Applying deep learning to text generation tasks (language modeling, chatbots)

Module #20
Deep Learning for Sentiment Analysis
Applying deep learning to sentiment analysis tasks

Module #21
Deep Learning for Information Retrieval
Applying deep learning to information retrieval tasks (search, recommendation systems)

Module #22
Deep Learning for Dialogue Systems
Applying deep learning to dialogue systems tasks (chatbots, conversational agents)

Module #23
Advanced Topics in Deep Learning for NLP
Advanced topics in deep learning for NLP (multi-task learning, transfer learning)

Module #24
Evaluating and Optimizing Deep Learning Models for NLP
Evaluating and optimizing deep learning models for NLP tasks

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Deep Learning Techniques for NLP career

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Deep Learning Techniques for NLP ( 25 Modules )

Deep Learning Techniques for NLP
( 25 Modules )