Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Lära Example of LSTM | Advanced RNN Variants
Introduction to RNNs
course content

Kursinnehåll

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 Data
4. Sentiment Analysis

book
Example of LSTM

In this chapter, we explore an example of how LSTM networks can be applied to time series prediction. The following graph illustrates the performance of Company A over a period of five months.

  • Data: The x-axis represents the Months, while the y-axis shows the performance metric (e.g., sales, revenue, etc.) ranging from 5 to 15.
  • Time Series Forecasting: An LSTM can be used to analyze the trend and predict future values based on past data. In the graph, we can see fluctuations, which LSTM will analyze to predict future months.
  • LSTM Application: Using past months' data, the LSTM network learns the pattern of increases and decreases in Company A's performance and can forecast future performance trends.

This is a typical application of LSTM in business forecasting, where past performance is used to predict future trends. The LSTM model learns from the time series data and can be used for more accurate predictions, especially when there are complex dependencies over time.

Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 2. Kapitel 4
Vi beklagar att något gick fel. Vad hände?
some-alt