Deep Learning for NLP
Master the art of leveraging deep learning within NLP with our comprehensive course designed for aspiring professionals. Embark on a transformative journey through text preprocessing, word embeddings, sentiment analysis, sequence modeling, transfer learning, and more while ensuring ethical AI practices.
Training Provider Pricing
Pax:
Features
Subsidies
What you'll learn
- Develop proficiency in text preprocessing techniques including tokenization, stemming, and lemmatization.
- Construct sequence to sequence models for machine translation and summarization.
- Explore advanced topics like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for NLP tasks.
- Recognize ethical considerations related to AI development in NLP.
- Learn about word embeddings and their role in representing semantic relationships between words.
- Understand the fundamental concepts of deep learning as they apply to NLP.
- Build practical skills by creating sentiment analysis models using CNNs.
- Implement transfer learning using state-of-the-art pre-trained language models for various NLP tasks.
Why should you attend?
Deep Learning for NLP is an immersive learning journey that delves into the intricate relationship between neural networks and natural language processing (NLP). This course provides a robust foundation in deep learning concepts, starting with basic neural networks and advancing through to convolutional and recurrent neural networks. Participants will gain hands-on experience in preprocessing text for NLP, mastering techniques such as tokenization, stemming, lemmatization, and stop word removal. The importance of cleaning and normalizing text data is underscored to ensure optimal model performance. The course further explores how numerical vectors are used to represent words through word embeddings, examining semantic similarity and popular embedding techniques like Word2Vec, GloVe, and FastText. Learners will build practical skills in sentiment analysis using deep learning on movie reviews datasets and understand the construction of sequence to sequence models for applications like machine translation. Named entity recognition using news articles datasets solidifies the application of recurrent neural networks. Transfer learning is demystified with hands-on exercises using pre-trained models such as BERT or GPT-2 for tasks including sentiment analysis and named entity recognition. Finally, the course addresses ethical considerations, emphasizing the developer's responsibility to mitigate bias and ensure their models are beneficial to society.
Course Syllabus
Short Break
15 minsShort Break
15 minsRecap and Q&A
15 minsLunch
1 hourShort Break
15 minsShort Break
15 minsShort Break
15 minsRecap and Q&A
15 minsEnd of Day 1
Short Break
15 minsShort Break
15 minsRecap and Q&A
15 minsLunch
1 hourShort Break
15 minsShort Break
15 minsShort Break
15 minsRecap and Q&A
15 minsEnd of Day 2
Why should you attend?
Deep Learning for NLP is an immersive learning journey that delves into the intricate relationship between neural networks and natural language processing (NLP). This course provides a robust foundation in deep learning concepts, starting with basic neural networks and advancing through to convolutional and recurrent neural networks. Participants will gain hands-on experience in preprocessing text for NLP, mastering techniques such as tokenization, stemming, lemmatization, and stop word removal. The importance of cleaning and normalizing text data is underscored to ensure optimal model performance. The course further explores how numerical vectors are used to represent words through word embeddings, examining semantic similarity and popular embedding techniques like Word2Vec, GloVe, and FastText. Learners will build practical skills in sentiment analysis using deep learning on movie reviews datasets and understand the construction of sequence to sequence models for applications like machine translation. Named entity recognition using news articles datasets solidifies the application of recurrent neural networks. Transfer learning is demystified with hands-on exercises using pre-trained models such as BERT or GPT-2 for tasks including sentiment analysis and named entity recognition. Finally, the course addresses ethical considerations, emphasizing the developer's responsibility to mitigate bias and ensure their models are beneficial to society.
What you'll learn
- Develop proficiency in text preprocessing techniques including tokenization, stemming, and lemmatization.
- Construct sequence to sequence models for machine translation and summarization.
- Explore advanced topics like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for NLP tasks.
- Recognize ethical considerations related to AI development in NLP.
- Learn about word embeddings and their role in representing semantic relationships between words.
- Understand the fundamental concepts of deep learning as they apply to NLP.
- Build practical skills by creating sentiment analysis models using CNNs.
- Implement transfer learning using state-of-the-art pre-trained language models for various NLP tasks.
Course Syllabus
Short Break
15 minsShort Break
15 minsRecap and Q&A
15 minsLunch
1 hourShort Break
15 minsShort Break
15 minsShort Break
15 minsRecap and Q&A
15 minsEnd of Day 1
Short Break
15 minsShort Break
15 minsRecap and Q&A
15 minsLunch
1 hourShort Break
15 minsShort Break
15 minsShort Break
15 minsRecap and Q&A
15 minsEnd of Day 2
Training Provider Pricing
Pax:
Features
Subsidies
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