Natural Language Processing

Learning Objectives Explain the concept of word embeddings and why they are superior to one-hot encoding. Differentiate between traditional NLP models (e.g., Bag-of-Words) and deep learning models. Describe the basic architecture of a Recurrent Neural Network (RNN) and its role in processing sequential data. Trace the flow of data through a simple neural network for a text classification task. Identify at least three common NLP tasks solvable with deep learning. Define key terms such as tokenization, activation function, and hidden state. Ever wonder how your phone's autocorrect seems to read your mind or how a website can instantly translate a sentence? 🤔 Let's explore the 'brain' behind this magic! This lesson introduces the powerful intersection of N...

TermDefinitionExample Word EmbeddingA method of representing words as dense, multi-dimensional numerical vectors. In this vector space, words with similar meanings have similar vector representations.The vector for 'king' minus the vector for 'man' plus the vector for 'woman' results in a vector very close to that of 'queen'. This shows the model has learned gender and royalty relationships. Neural NetworkA computational model inspired by the structure of the human brain, consisting of interconnected nodes (neurons) organized in layers. It learns to perform tasks by adjusting the connection strengths (weights) between neurons based on training data.A simple network for sentiment analysis might have an input layer for word vectors, one or more '...

The NLP Data Pipeline Raw Text -> Tokenization -> Vocabulary Mapping -> Vectorization (Embeddings) This is the fundamental sequence for preparing text data for any deep learning model. You must convert unstructured text into a structured numerical format (vectors/tensors) that the model can process. RNN State Update Formula h_t = f(W * h_{t-1} + U * x_t) This rule describes the core logic of an RNN. The new hidden state (h_t) is a function (f) of the previous hidden state (h_{t-1}) and the current input (x_t), transformed by weight matrices (W and U). It shows how memory from the past is combined with new information. Softmax for Classification Softmax(z_i) = e^(z_i) / Σ(e^(z_j)) for all j Used in the final layer of a multi-class classification model. It tak...