14 docs tagged with "nlp"

How the Attention mechanism solved the bottleneck problem in Seq2Seq models and paved the way for Transformers.

A deep dive into the GRU architecture, its update and reset gates, and how it compares to LSTM.

Intermediate-level ML projects focusing on NLP, Computer Vision, and Time-Series forecasting.

Mastering JSON for Machine Learning: handling nested data, converting dictionaries, and efficient parsing for NLP pipelines.

Understanding how to return words to their dictionary base forms using morphological analysis.

A deep dive into the LSTM architecture, cell states, and the gating mechanisms that prevent vanishing gradients.

Understanding how multiple attention 'heads' allow Transformers to capture diverse linguistic and spatial relationships simultaneously.

An introduction to Recurrent Neural Networks, hidden states, and processing sequential data.

Learn how to normalize text by stripping suffixes to find the base form of words.

Understanding how models weigh the importance of different parts of an input sequence using Queries, Keys, and Values.

The first step in NLP: Converting raw text into manageable numerical pieces.

A comprehensive deep dive into the Transformer architecture, including Encoder-Decoder stacks and Positional Encoding.

How to represent words as dense vectors where geometric distance corresponds to semantic similarity.

Transforming raw text into numerical features using Bag of Words, TF-IDF, and Scikit-Learn's feature extraction tools.