Here are the slides for the "Deep Learning for NLP" course. These slides cover the fundamentals of deep learning applied to natural language processing.

Table of Contents

  • Introduction to Deep Learning
  • Basics of NLP
  • Word Embeddings
  • Recurrent Neural Networks (RNNs)
  • Long Short-Term Memory Networks (LSTMs)
  • Convolutional Neural Networks (CNNs)
  • Transformers and BERT
  • Practical Applications

Introduction to Deep Learning

Deep learning is a subset of machine learning that involves neural networks with many layers. These networks can learn complex patterns in data.

Deep Learning Neural Network

Basics of NLP

Natural Language Processing (NLP) is the field of AI that focuses on the interaction between computers and humans through natural language.

  • Tokenization: Splitting text into words or phrases.
  • Part-of-Speech Tagging: Labeling words with their grammatical properties.
  • Named Entity Recognition: Identifying and classifying named entities in text.

Word Embeddings

Word embeddings are vectors that represent words in a dense vector space. They capture the semantic meaning of words.

  • Word2Vec: A method for learning word embeddings.
  • GloVe: Global Vectors for Word Representation.

Recurrent Neural Networks (RNNs)

RNNs are a type of neural network that processes sequences of data. They are particularly useful for NLP tasks.

  • Simple RNN: Basic RNN architecture.
  • LSTM: A type of RNN that can learn long-term dependencies.

Convolutional Neural Networks (CNNs)

CNNs are primarily used for image recognition, but they can also be applied to NLP tasks, such as text classification.

Transformers and BERT

Transformers are a type of neural network architecture that have become popular for NLP tasks. BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained language model that can be fine-tuned for various NLP tasks.

  • Transformer Architecture: Overview of the transformer model.
  • BERT: How BERT works and its applications.

Practical Applications

Deep learning has many practical applications in NLP, such as:

  • Text Classification: Categorizing text into predefined categories.
  • Sentiment Analysis: Determining the sentiment of a text.
  • Machine Translation: Translating text from one language to another.

For more information on deep learning and NLP, please visit our Deep Learning Tutorial.