Master Deep Learning for Natural Language Processing: From Basics to BERT
What you will learn:
- Develop a comprehensive understanding of both traditional and deep learning NLP techniques.
- Gain practical experience applying deep learning to NLP problems (sentiment analysis, machine translation, chatbots, question answering).
- Master state-of-the-art NLP models like BERT and GPT.
- Understand the evolution of word and sentence embeddings (word2vec, GloVe, FastText, ELMo, BERT).
- Become proficient in using transfer learning in modern NLP models.
Description
Embark on a transformative journey into the realm of Natural Language Processing (NLP) with this comprehensive deep learning course. We'll guide you from fundamental text preprocessing techniques and traditional models to the forefront of NLP innovation, culminating in a deep understanding of BERT and other transformer networks. Explore the intricacies of word and sentence embeddings, mastering techniques like word2vec, GloVe, FastText, and ELMo. Delve into the power of recurrent neural networks (RNNs), LSTMs, GRUs, and sequence-to-sequence models for tasks like machine translation and chatbot development. Discover how collaborative filtering and twin-tower models leverage embeddings for advanced recommender systems. We'll unravel the complexities of attention mechanisms and the revolutionary architecture of transformer networks, building a solid foundation for working with state-of-the-art models such as BERT, GPT, RoBERTa, ALBERT, XLNet, and more. Finally, learn to harness the power of transfer learning in NLP to solve real-world problems efficiently. This course is your pathway to mastering the latest advancements in deep learning for NLP.
Curriculum
Module 1: NLP Foundations
This introductory module lays the groundwork for understanding NLP challenges. You'll explore the complexities of natural language, common NLP tasks and applications, and the parallels between Computer Vision and NLP. We'll then cover the Bag-of-Words model, the crucial text preprocessing pipeline (including cleaning and feature extraction), and various text feature types such as binary, count, frequency, and TF-IDF.
Module 2: Word Embeddings and Deep Learning
Module 2 delves into the world of word embeddings, explaining their importance for representing words semantically. You'll explore traditional word vectors, the concept of learnable embedding matrices, and the Bag-of-Words model in detail. We'll then cover structured deep learning and examine popular pre-trained word embedding techniques like word2vec, GloVe, FastText, and ELMo. The module concludes with an evaluation of these vector representations.
Module 3: Recommender Systems using Embeddings
This module demonstrates the broader applications of embeddings by applying them to recommender systems. You'll learn about content-based recommendations and the powerful technique of collaborative filtering, seeing how word embeddings can be adapted to solve real-world problems beyond natural language.
Module 4: Sequence Modeling with RNNs
This module explores sequence models, starting with statistical and neural language models. You will master recurrent neural networks (RNNs), including LSTMs and GRUs, learning to use them as sentence embedding encoders. Practical examples demonstrate RNNs for character and word-level language modeling. The module covers essential language model evaluation methods and presents applications such as text classification using LSTM/GRU and Conv1D/CNN-LSTM models.
Module 5: Sequence-to-Sequence Models
Here, we move to sequence-to-sequence models, covering their overview and various use cases. You'll learn about handling unaligned and matched sequences using Connectionist Temporal Classification (CTC) loss. The module explores statistical and neural machine translation (SMT and NMT), the vanilla seq2seq model, decoding strategies like beam search, and the integration of attention mechanisms for improved performance.
Module 6: Transformer Networks
This module dives into the groundbreaking Transformer network architecture, exploring self-attention, multi-head attention, and the encoder-decoder structure. You'll learn to evaluate seq2seq models using metrics like Word Error Rate (WER) and BLEU score. This module provides the foundation for understanding the advanced models discussed in the next module.
Module 7: Transfer Learning in NLP
The final module focuses on transfer learning, a technique crucial for modern NLP. You'll explore word-level and sentence-level transfer learning. We'll then delve into the powerful pre-trained transformer architectures like BERT, GPT, XLNet, and others, concluding with a discussion of gigantic transformer models and knowledge distillation methods like DistillBERT.
Module 8: Resources
This module provides access to supplementary materials, including notebooks and additional resources to enhance your learning experience.
