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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

> **Note:** Humans don’t start their thinking from scratch every second. As you read this essay, you understand each word based on your understanding of previous words. You don’t throw everything away and start thinking from scratch again. Your thoughts have persistence (Olah 2015).

# Recurrent neural networks (RNN)

Sequences and time series processes are like essays. The order of words in an essay and, likewise, the observations in sequences and time series processes are essential. Due to this, they have temporal patterns. This means the previous observations (the memory) have an effect on the future.
Memory persistence is one approach to learning such temporal patterns. **Recurrent neural networks (RNNs)**, such as long-term and short-term memory networks, were conceptualized for this purpose.

RNNs constitute a very powerful class of computational models capable of learning arbitrary dynamics. They work by keeping a memory of patterns in sequential orders. They combine knowledge from memories with current information to make a prediction.
RNN development can be traced back to Rumelhart, G. E. Hinton, and R. J. Williams 1985. Several RNN variants have been developed since then. For example, Elman 1990, Jordan 1990, and time-delay neural networks by Lang, Waibel, and G. E. Hinton 1990.
However, most of the RNNs became obsolete because of their inability to learn long-term memories. This was due to the vanishing gradient issue, which we’ll discuss later in this course.

# Long short-term memory 








Learn about the essentials of LSTM networks, exploring their unique ability to capture long-term dependencies in sequential data. 

Getting Started

Rare Event Prediction

Multi-Layer Perceptrons (MLPs)

Long Short-Term Memory (LSTM) Networks

Convolutional Neural Networks (CNNs)

Autoencoders

Conclusion

Understanding Long Short-Term Memory Networks

Recurrent neural networks (RNN)