Summary  
This chapter introduces methods to convert raw text into numerical vectors—such as bag-of-words, one-hot encoding, TF-IDF, and word embeddings—to prepare text data for machine learning models.

General domain of usage  
Natural Language Processing

Different **text encoding** schemes are explored to transform raw text into numerical representations suitable for machine learning algorithms. Text encoding is a crucial step in NLP, enabling the conversion of unstructured text into structured formats that capture meaning and word relationships.


- The process of representing text as an unordered set of words;
- Each word is assigned a unique index and represented as a vector with word counts;
- Advantages: simple to understand and implement;
- Disadvantages: ignores word order and context, losing semantic meaning.

Bag of words (BOW)

- TF-IDF considers the frequency of words in a document and the importance across all documents;
- Term frequency (TF) measures how often a word appears in a document;
- Inverse document frequency (IDF) measures how important a word is by down-weighting common words;
- Advantages: reduces the weight of common words and emphasizes more informative terms;
- Disadvantages: ignores word order and context, but provides more relevant information than BOW.

Term frequency-inverse document frequency (TF-IDF)

- Represents each word as a binary vector with a 1 at the index corresponding to the word;
- The size of the vector equals the total number of unique words in the corpus;
- Advantages: simple and works well for smaller datasets;
- Disadvantages: results in sparse vectors and does not capture word relationships or semantic meaning.

One-hot encoding

- Represents words as dense vectors in a continuous vector space;
- Words with similar meanings are closer together in the vector space;
- Common methods include Word2Vec and GloVe, which capture semantic relationships between words;
- Advantages: captures word meanings, context, and relationships, making it more powerful than BOW or TF-IDF;
- Disadvantages: requires more computational resources and a large dataset to train, though pre-trained embeddings are available.

Word embeddings

In summary, text encoding is an essential part of preprocessing text data for NLP tasks. While simpler methods like BOW and TF-IDF are useful for certain tasks, **word embeddings** offer a more powerful and semantically rich representation of words, which will be essential in more advanced NLP tasks, such as sentiment analysis. Later, we will implement **word embeddings** for our sentiment analysis project to capture the context and meaning of words more effectively.

In **TF-IDF** encoding, what does the "Inverse Document Frequency" (IDF) component measure?

Master Recurrent neural networks and their advanced variants like LSTMs and GRUs using PyTorch. Gain hands-on experience processing sequential data for practical applications. Apply these powerful models to tackle real-world challenges in time series forecasting and various Natural language processing tasks.

Covers the limitations of traditional neural networks for sequential data and introduces the fundamentals of Recurrent Neural Networks. Explains RNN architecture, types, and step-by-step implementation through basic examples and a coding challenge.

Explores common training challenges such as vanishing and exploding gradients. Introduces advanced RNN variants including LSTM and GRU, highlighting their internal mechanisms and use cases, with practical implementation examples for each.

Focuses on processing and forecasting time series data using RNN-based models. Includes data loading, preprocessing techniques, model training, and performance evaluation, with emphasis on comparing LSTM and GRU architectures.

Demonstrates the application of RNNs to text classification tasks. Covers core NLP concepts, text encoding methods, data preparation steps, and construction of an LSTM-based model for sentiment prediction.

Text Encoding