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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.

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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

# Loss function

A rare event problem has very few positively labeled samples. Due to this, even if the classifier misclassifies the positive labels, their effect on the loss function is minuscule.

$$\mathcal{L(\theta)}=−\frac{1}{n}\sum_{i=1}^{n}\big( y_{i}log(p_{i})+(1−y_{i})log(1−p_{i})\big)$$

 where  
  * $y_{i} ∈ 0,1$ are the true labels.
  * $p_{i} = \text{Pr}[y_{i} = 1]$ is the predicted probability for $y_{i} = 1$ .

Remember the loss function in the equation above; it gives equal importance to the positive and negative samples. We can overweight the positives and underweight the negative samples to overcome this. A binary cross-entropy loss function will then be:

$$\mathcal{L(\theta)}=−\frac{1}{n} \sum_{i=1}^{n} \big(w_{1}y_{i}log(p_{i})+w_{0}(1−y_{i})\log(1−p_{i})\big)$$

where
* $w_1 > w_0$.
* $y_{i} ∈ 0,1$ are the true labels.
* $p_{i} = \text{Pr}[y_{i} = 1]$ is the predicted probability for $y_{i} = 1$.

# Class-weighting approach
The class-weighting approach works as follows:

* The model estimation objective is to minimize the loss. In a perfect case, if the model could predict all the labels perfectly, that is, $p_i = 1|y_i = 1$ **and** $p_i = 0|y_i = 0$, the loss will be **zero**. Therefore, the best model estimate is the one with the loss closest to zero.

* With the class weights, $w_1 > w_0$, if the model misclassifies the positive samples, that is, $p_i → 0|y_i = 1$, the loss goes **farther away from zero** as compared to if the negative samples are misclassified. In other words, the model training penalizes the misclassification of positives more than negatives.

* Therefore, the model estimation strives to classify the minority positive samples correctly.

In principle, any arbitrary weights such that $w_1 > w_0$ can be used. But a rule-of-thumb is:

* $w_1$:
  $$\text{positive class weight} = \frac{\text{number of negative samples}} {\text{total samples}}$$

* $w_0$: 
  $$\text{negative class weight} = \frac{\text{number of positive samples}} {\text{total samples}}$$









Discover how adjusting class weights in neural network models can address the challenge of predicting rare events. 

Getting Started

Rare Event Prediction

Multi-Layer Perceptrons (MLPs)

Long Short-Term Memory (LSTM) Networks

Convolutional Neural Networks (CNNs)

Autoencoders

Conclusion

Refining Neural Networks with Class Weights

Loss function