Deep Learning for NLP - Part 2

🎓 Course Title: Deep Learning for NLP - Part 2: Encoder-decoder models, attention and Transformers

🚀 Course Headline: Unlock the Secrets of Advanced NLP with Encoder-decoder Models, Attention Mechanisms, and Transformers!

Course Description:

Dive deeper into the world of Natural Language Processing (NLP) with our comprehensive online course, "Deep Learning for NLP - Part 2". This course builds upon foundational knowledge from our previous sessions and introduces you to state-of-the-art deep learning models that are revolutionizing NLP today. Manish Gupta, an experienced instructor, will guide you through the intricacies of Encoder-decoder attention models, ELMo, GLUE benchmark tasks, Transformers, GPT, and BERT.

What You'll Learn:

🔥 Section 1: Encoder-decoder Models & Attention Mechanisms

• Understanding Encoder-decoder models in the context of machine translation.
• Exploring the principles behind the beam search decoder and how it enhances model performance.
• Learning about encoder-decoder attention, including its various forms: Global, local, hierarchical, and sentence pairs with CNNs/LSTMs.
• Gaining insights into attention visualization and its role in interpreting model decisions.
• Exploring ELMo - a breakthrough approach for context-sensitive word representations that leverage recurrent neural networks (RNNs).

🌐 Section 2: Transformers & Modern NLP Models

• A comprehensive look at the GLUE benchmark and other key NLP datasets.
• An in-depth analysis of the Transformer architecture, including self attention, multi-head attention, positional embeddings, residual connections, and masked attention.
• Discovering the inner workings of two transformative models: GPT (Generative Pretrained Transformer) and BERT (Bidirectional Encoder Representations from Transformers).
  • Understand the training processes behind GPT variants like GPT2, GPT3, and their differences.
  • Learn how BERT differs from GPT, its pretraining methodology using masked language modeling, and next sentence prediction tasks.
  • Master the fine-tuning process for BERT and explore multilingual models like multilingual BERT (mBERT).

Why Take This Course?

• Practical Deep Dive: This course offers a hands-on approach to learning, with real-world applications and examples.
• Cutting-Edge Knowledge: Stay ahead of the curve by understanding the most advanced models in NLP.
• Expert Guidance: Learn from an instructor with deep expertise in the field, ensuring you receive accurate and up-to-date information.
• Community Engagement: Join a community of learners and professionals interested in advancing their NLP skills.

🚀 Enroll Now to embark on your journey through the complex and fascinating landscape of Deep Learning for Natural Language Processing!

📚 Key Takeaways:

• A thorough understanding of Encoder-decoder models and attention mechanisms in NLP.
• In-depth knowledge of the Transformer model, its components, and its significance.
• Practical insights into GPT, BERT, and how they are revolutionizing the field of NLP.
• Techniques for fine-tuning models to suit specific tasks or languages.

🧠 Who Should Take This Course?

• Data Scientists and Machine Learning Engineers looking to specialize in NLP.
• Students and researchers interested in deep learning and its applications in language technology.
• Professionals aiming to enhance their expertise in AI and machine learning with a focus on language understanding and generation.

🎓 Elevate your NLP skills and unlock the full potential of language models!