Deep Learning for NLP - Part 3

🎉 Deep Learning for NLP - Part 3: Sentence Embeddings, Generative Transformer Models 🧠🤖

Welcome to the third installment of our comprehensive series on "Deep Learning for NLP" where we dive deep into the realm of Natural Language Processing (NLP) using advanced deep learning techniques. As a seasoned learner, you'll embark on a journey through complex models and emerge with a mastery of sentence embeddings and Generative Transformer Models that are shaping the future of NLP.

Course Overview: In this course, led by expert instructor Manish Gupta, you will:

• 🧠 Understand Sentence Embeddings: Discover how to represent sentences in a way that captures their semantic meaning, using various methods from simple bag of words to sophisticated models like Doc2Vec and SkipThought. Learn the ins and outs of unsupervised and supervised techniques, including recursive neural networks, deep averaging networks, and InferSent.

• 🧪 Explore Multi-Task Learning Methods: Dive into multi-task learning frameworks that leverage sentence embeddings for improved performance in NLP tasks, with a special focus on Universal Sentence Encodings and MT-DNN.

• ✨ Master Generative Transformer Models: Gain hands-on experience with state-of-the-art generative models like UniLM, Transformer-XL, XLNets, MASS, BART, CTRL, T5, and ProphetNet. Discover their unique architectures and applications in NLP.

Course Structure: The course is structured into two main sections:

1. Sentence Embeddings (SE):

   • Basic concepts of sentence embeddings and their importance in NLP.
   • A detailed exploration of methods like averaged bag of words, word mover's distance, SIF and Power means method, Doc2Vec, SkipThought, recursive neural networks, deep averaging networks, InferSent, DSSSM for semantic similarity, and multi-task learning approaches.
   • Advanced discussions on Universal Sentence Encodings and MT-DNN.
   • Concluding with an in-depth look at SentenceBERT, a robust sentence encoding model that outperforms BERT on several NLP tasks.

2. Generative Transformer Models (GTM):

   • Introduction to the transformer models and their significance in NLP.
   • A comprehensive study of UniLM and its architecture.
   • Insights into segment recurrence and relative position embeddings in Transformer-XL.
   • Exploring XLNets and permutation language modeling.
   • Understanding span masking and noising methods in BART.
   • Delving into controlled natural language generation with CTRL.
   • Learning how T5 treats every learning task as a text-to-text problem.
   • Finalizing the course with ProphetNet's n-stream attention modeling, enabling n-gram predictions.

Why Take This Course?

• Gain a deep understanding of sentence embeddings and generative transformer models in NLP.
• Learn from an expert instructor who brings years of experience and insights into the field.
• Access to hands-on projects and real-world examples that will solidify your understanding and application of these concepts.
• Join a community of learners who are as passionate about NLP as you are.

Enroll now and take your first step towards mastering Deep Learning for NLP! 🚀📚