Deep Learning for Natural Language Understanding
( 25 Modules )

Module #1
Introduction to Natural Language Processing
Overview of NLP, importance of deep learning in NLP, and course objectives

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

Module #3
Introduction to Deep Learning
Basic concepts of deep learning, including neural networks, activation functions, and backpropagation

Module #4
Deep Learning for NLP:Fundamentals
Understanding how deep learning is applied to NLP, including word embeddings and sequence data

Module #5
Word Embeddings
In-depth coverage of word embeddings, including Word2Vec and GloVe

Module #6
Recurrent Neural Networks (RNNs)
Introduction to RNNs, including simple RNNs, GRUs, and LSTMs

Module #7
RNNs for NLP
Applications of RNNs in NLP, including language modeling and text classification

Module #8
Long Short-Term Memory (LSTM) Networks
In-depth coverage of LSTMs, including architecture and applications

Module #9
Gated Recurrent Units (GRUs)
In-depth coverage of GRUs, including architecture and applications

Module #10
Convolutional Neural Networks (CNNs) for NLP
Introduction to CNNs and their applications in NLP, including text classification and sentiment analysis

Module #11
Attention Mechanisms
Introduction to attention mechanisms, including self-attention and multi-head attention

Module #12
Transformer Models
In-depth coverage of transformer models, including architecture and applications

Module #13
Language Models
Introduction to language models, including statistical language models and neural language models

Module #14
Text Classification
Applications of deep learning in text classification, including sentiment analysis and topic modeling

Module #15
Named Entity Recognition (NER)
Introduction to NER, including traditional approaches and deep learning-based methods

Module #16
Dependency Parsing
Introduction to dependency parsing, including traditional approaches and deep learning-based methods

Module #17
Sequence-to-Sequence Models
In-depth coverage of sequence-to-sequence models, including machine translation and text summarization

Module #18
Question Answering
Introduction to question answering, including traditional approaches and deep learning-based methods

Module #19
Sentiment Analysis
Applications of deep learning in sentiment analysis, including aspect-based sentiment analysis

Module #20
Deep Learning for Dialogue Systems
Introduction to deep learning for dialogue systems, including chatbots and conversational agents

Module #21
Natural Language Generation
Introduction to natural language generation, including text generation and language translation

Module #22
Deep Learning for Multimodal Language Understanding
Introduction to deep learning for multimodal language understanding, including vision-language tasks

Module #23
Specialized Deep Learning Models for NLP
Coverage of specialized deep learning models for NLP, including graph-based models and capsule networks

Module #24
Deep Learning for Low-Resource Languages
Introduction to deep learning for low-resource languages, including language transfer and multilingual models

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Deep Learning for Natural Language Understanding 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 Understanding ( 25 Modules )

Deep Learning for Natural Language Understanding
( 25 Modules )