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Gain insights into deep learning constructs like LSTM networks and autoencoders. Delve into modeling, prediction strategies, and TensorFlow implementation for rare event prediction in real-life scenarios.

Understanding Deep Learning Application in Rare Event Prediction.png

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This course aims to provide a practical understanding of the key constructs of deep learning, including Multi-layer Perceptrons, Long-Short-Term Memory (LSTM) networks, convolutional neural networks, and autoencoders, which focus on rare event prediction.

Through this course, you’ll gain hands-on experience developing solutions for rare event prediction. You’ll start by modeling rare events that occur infrequently. Next, you’ll explore machine and deep learning solutions for imbalanced data. You’ll then learn about rare event “prediction,” its statistical foundations, and prediction strategies. Finally, you’ll implement real-life prediction models using TensorFlow, a crucial framework for building and training models.

By the end of this course, you’ll have a strong grasp of advanced machine and deep learning techniques in rare event prediction. These exercises will not only help you solve complex issues but also become a firm footing for further study and innovation in this field.

Understanding Deep Learning Applications in Rare Event Prediction

It’s always advisable to begin with a baseline model. A restricted stateless LSTM network is taken as a baseline. In such a network, every LSTM layer is stateless, and the final layer has a restricted output, that is:
```python
LSTM(..., stateful=False, return_sequences=False)
```

We’ll now proceed to build the baseline model, outlining each step in the process. 

# Input layer

The input layer in LSTM expects three-dimensional inputs. The input shape should be: 

$$\textit{(batch size, time-steps, features)}$$

A stateless LSTM doesn’t require to specify the batch size explicitly. Therefore, the input shape is defined as follows in the code below.

Learn to build and analyze a restricted stateless LSTM network as your baseline model for time series analysis.

Getting Started

Rare Event Prediction

Multi-Layer Perceptrons (MLPs)

Long Short-Term Memory (LSTM) Networks

Convolutional Neural Networks (CNNs)

Autoencoders

Conclusion

Restricted Stateless LSTM Network for Baseline Modeling

Input layer