Deep Learning for Natural Language Processing
( 25 Modules )

Module #1
Introduction to NLP
Overview of Natural Language Processing and its applications

Module #2
Deep Learning Fundamentals
Review of deep learning concepts, including neural networks, activation functions, and backpropagation

Module #3
NLP Task Types
Classification of NLP tasks, including language modeling, text classification, and sequence-to-sequence tasks

Module #4
Text Preprocessing
Text preprocessing techniques, including tokenization, stemming, and lemmatization

Module #5
Word Embeddings
Introduction to word embeddings, including Word2Vec and GloVe

Module #6
Language Modeling
Introduction to language modeling, including statistical language models and neural language models

Module #7
Recurrent Neural Networks (RNNs)
Introduction to RNNs, including simple RNNs, LSTM, and GRU

Module #8
Long Short-Term Memory (LSTM) Networks
In-depth exploration of LSTM networks

Module #9
Gated Recurrent Units (GRU)
In-depth exploration of GRU networks

Module #10
Text Classification
Introduction to text classification, including binary and multi-class classification

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

Module #12
Recurrent Convolutional Neural Networks (RCNNs)
Introduction to RCNNs, including text classification and language modeling

Module #13
Attention Mechanism
Introduction to attention mechanism, including self-attention and cross-attention

Module #14
Transformers
In-depth exploration of transformers, including BERT and RoBERTa

Module #15
Sequence-to-Sequence Tasks
Introduction to sequence-to-sequence tasks, including machine translation and text generation

Module #16
Named Entity Recognition (NER)
Introduction to NER, including named entity recognition and entity disambiguation

Module #17
Part-of-Speech (POS) Tagging
Introduction to POS tagging, including rule-based and machine learning approaches

Module #18
Dependency Parsing
Introduction to dependency parsing, including constituency parsing and dependency grammar

Module #19
Sentiment Analysis
Introduction to sentiment analysis, including binary and multi-class sentiment analysis

Module #20
Question Answering
Introduction to question answering, including extractive and abstractive question answering

Module #21
Language Translation
Introduction to language translation, including statistical and neural machine translation

Module #22
Text Generation
Introduction to text generation, including language models and sequence-to-sequence models

Module #23
Deep Learning Architectures for NLP
Exploration of advanced deep learning architectures for NLP, including graph neural networks and capsule networks

Module #24
NLP with PyTorch
Hands-on implementation of NLP tasks using PyTorch

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Deep Learning for Natural Language Processing career

WIZAPE

ONLINE RESOURCES

ABOUT WIZAPE

ONLINE PORTAL

Our priority is to cultivate a vibrant community before considering the release of a token. By focusing on engagement and support, we can create a solid foundation for sustainable growth. Let’s build this together!

We're giving our website a fresh new look and feel! 🎉 Stay tuned as we work behind the scenes to enhance your experience.
Get ready for a revamped site that’s sleeker, and packed with new features. Thank you for your patience. Great things are coming!

CONTACT-US PRIVACY POLICY

Deep Learning for Natural Language Processing ( 25 Modules )

Deep Learning for Natural Language Processing
( 25 Modules )