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Introduction to RNNs
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Introduction to RNNs

Introduction to RNNs

1. Introduction to RNNs
Shortcomings of ANNsHow RNN Works?Types of RNNsImplementing a Basic RNN
2. Advanced RNN Variants
Vanishing and Exploding GradientsSigmoid and Tanh Activations Long Short-Term Memory (LSTM) NetworksExample of LSTMGated Recurrent Units (GRU)When to Use LSTM and GRU
3. Time Series Analysis
Introduction to Time Series ForecastingLoading the DataPreprocessing Time Series DataTraining and Evaluating an RNNLSTM vs GRU for Time Series
4. Sentiment Analysis
NLP BasicsText EncodingLoading and Preprocessing the DataSentiment Analysis Model

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Text Encoding

In this chapter, we explore different text encoding schemes that transform raw text into numerical representations that can be processed by machine learning algorithms. Text encoding is a crucial step in NLP, as it allows us to convert unstructured text data into structured formats that capture the meaning and relationships between words.

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.

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

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Seção 4. Capítulo 2
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