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Introduction to RNNs
course content

Conteúdo do Curso

Introduction to RNNs

Introduction to RNNs

1. Introduction to RNNs
Shortcomings of ANNsHow RNN Works?Types of RNNsImplementing a Basic RNNChallenge: Implementing the 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 SeriesChallenge: Time Series Forecasting with LSTM
4. Sentiment Analysis
NLP BasicsText EncodingLoading and Preprocessing the DataSentiment Analysis ModelChallenge: Building an LSTM for Sentiment Analysis

book
LSTM vs GRU for Time Series

In this chapter, we are going to learn about the comparison highlighting the key differences between Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks.

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Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 3. Capítulo 5

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course content

Conteúdo do Curso

Introduction to RNNs

Introduction to RNNs

1. Introduction to RNNs
Shortcomings of ANNsHow RNN Works?Types of RNNsImplementing a Basic RNNChallenge: Implementing the 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 SeriesChallenge: Time Series Forecasting with LSTM
4. Sentiment Analysis
NLP BasicsText EncodingLoading and Preprocessing the DataSentiment Analysis ModelChallenge: Building an LSTM for Sentiment Analysis

book
LSTM vs GRU for Time Series

In this chapter, we are going to learn about the comparison highlighting the key differences between Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks.

Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 3. Capítulo 5
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