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This course teaches how to build deep learning models using TensorFlow to predict rare probability events.

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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

Convolution is a cornerstone operation in deep learning, particularly in the field of image processing and recognition. The concept might seem intricate at first glance, but it becomes more approachable when illustrated through practical examples. In this lesson, we'll demystify convolution by exploring how it's applied in a common deep learning task: image recognition.

# Problem

Consider an image of the number “2.” To a human, identifying this numeral is almost instantaneous, but for a machine, the task requires a systematic approach to recognize patterns and shapes that define “2.” This is where convolution comes into play. The objective and challenge are to train a machine to perform the task, for which one approach is “filtration.”

Learn to apply convolution in image recognition and discover pattern detection techniques. 

Getting Started

Rare Event Prediction

Multi-Layer Perceptrons (MLPs)

Long Short-Term Memory (LSTM) Networks

Convolutional Neural Networks (CNNs)

Autoencoders

Conclusion

Decoding Convolution

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