Summary  
This chapter covers the mathematical behavior and implementation of the sigmoid and tanh activation functions to squash inputs into fixed ranges and control information flow in a neural network’s memory units.  

General domain of usage  
Recurrent neural networks (RNNs)

The **sigmoid** and **tanh** activation functions are explored, as they play a crucial role in the functioning of **RNNs**. These functions transform inputs into outputs, enabling the model to make predictions.


- **Sigmoid activation**: the sigmoid function maps input values to an output range between 0 and 1. It is commonly used in binary classification tasks, as its output can be interpreted as a probability. However, it suffers from the **vanishing gradient problem** when the input values are very large or very small;  
- **Tanh activation**: the **tanh** function is similar to the sigmoid but maps the input values to an output range between -1 and 1. It helps center the data around zero, which can aid learning. Despite its benefits, it also suffers from the vanishing gradient problem in certain situations;  
- **Working of sigmoid and tanh**: both functions work by squashing the input values into a bounded range. The primary difference lies in their output range: **sigmoid** (0 to 1) vs. **tanh** (-1 to 1), which affects how the network processes and updates the information.

In the next chapter, we will look at how these activation functions play a role in **LSTM** networks and how they help overcome some of the limitations of standard RNNs.


What is the output range of the sigmoid activation function?

Master Recurrent neural networks and their advanced variants like LSTMs and GRUs using PyTorch. Gain hands-on experience processing sequential data for practical applications. Apply these powerful models to tackle real-world challenges in time series forecasting and various Natural language processing tasks.

Covers the limitations of traditional neural networks for sequential data and introduces the fundamentals of Recurrent Neural Networks. Explains RNN architecture, types, and step-by-step implementation through basic examples and a coding challenge.

Explores common training challenges such as vanishing and exploding gradients. Introduces advanced RNN variants including LSTM and GRU, highlighting their internal mechanisms and use cases, with practical implementation examples for each.

Focuses on processing and forecasting time series data using RNN-based models. Includes data loading, preprocessing techniques, model training, and performance evaluation, with emphasis on comparing LSTM and GRU architectures.

Demonstrates the application of RNNs to text classification tasks. Covers core NLP concepts, text encoding methods, data preparation steps, and construction of an LSTM-based model for sentiment prediction.

Sigmoid and Tanh Activations

Awesome!

Sigmoid and Tanh Activations