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Understanding Deep Learning Applications in Rare Event Prediction

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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Overview

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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.

This course aims to provide a practical understanding of the key constructs of deep learning, including Multi-layer Perceptrons,...Show More

WHAT YOU'LL LEARN

An understanding of deep learning fundamentals, including Multi-layer Perceptrons, LSTMs, convolutional neural networks, and autoencoders

Hands-on experience modeling rare events

The ability to build and train deep learning models using TensorFlow that predict rare events

Hands-on experience implementing LSTMs for capturing long-term dependencies in sequential data

An understanding of deep learning fundamentals, including Multi-layer Perceptrons, LSTMs, convolutional neural networks, and autoencoders

Content

59 Lessons6 Quizzes

Getting Started

6 Lessons

Get familiar with applying deep learning for predicting rare, impactful events with tailored evaluation metrics.

Introduction

Predictive Patterns and Rare Event Prediction

The Impact and Prediction of Rare Events

Tackling the Paper Sheet-Break Challenge

Machine and Deep Learning Solutions for Imbalanced Data

Quiz: Getting Started

Rare Event Prediction

3 Lessons

Unpack the core of statistical foundations, prediction strategies, and spatio-temporal challenges in rare event prediction.

Statistical Foundations and Prediction Strategies

Tackling Spatio-Temporal Challenges

Quiz: Rare Event Prediction

Multi-Layer Perceptrons (MLPs)

12 Lessons

Examine foundational concepts, data preparation, neural network construction, regularization, custom functions, and advanced metrics in MLPs.

Unraveling the Basics of Neural Networks

Understanding Multi-Layer Perceptrons

Data Preparation and Scaling

Neural Network Construction

Using Dropout to Combat Overfitting

Refining Neural Networks with Class Weights

Activating Neural Network Potential with Advanced Functions

Custom Activation Functions

Enhancing Model Selection with Custom Metrics

Optimizing and Evaluating the Final Neural Network Model

Exercise: Applying Batch Normalization in Neural Networks

Quiz: Multi-Layer Perceptrons (MLPs)

Long Short-Term Memory (LSTM) Networks

11 Lessons

Enhance your skills in LSTM networks for advanced sequential data modeling and prediction.

Understanding Long Short-Term Memory Networks

Intricacies of Long Short-Term Memory Cells

Understanding LSTM Activations and Stabilized Gradients

LSTM Layer Operations and Network Architectures

Preparing and Analyzing Time Series Data with LSTM Models

Restricted Stateless LSTM Network for Baseline Modeling

Unrestricted LSTM with Dropouts and Backward Input

Bidirectional LSTM for Enhanced Temporal Learning

Advanced Techniques in LSTM Models

Exercise: Exploring Activation Functions in LSTM Models

Quiz: Long Short-Term Memory (LSTM) Networks

Convolutional Neural Networks (CNNs)

17 Lessons

Take a look at CNNs for efficient high-dimensional data handling, spatial feature extraction, and time series modeling.

Convolutional Networks in the High-Dimensional Data Maze

Decoding Convolution

Understanding Parameter Sharing, Weak Filters, and Equivariance

Role of Pooling Layers in Convolutional Network Regularization

Kernels in Convolutional Network Operations

Padding, Strides, and Dilation

Architecture of Convolutional Networks

Applying Convolutional Networks to Multivariate Time Series

Modeling Multivariate Time Series

Efficient Pooling Strategies

Maximizing Efficiency with Complete Statistics

Strategic Pooling Approaches

Optimizing Feature Map Pooling

Advancing Pooling Techniques

Constructing an Optimized Baseline Convolutional Network

Exercise: Time Series Analysis with Longer Lookbacks

Quiz: Convolution Neural Networks

Autoencoders

8 Lessons

See how it works to build and optimize diverse autoencoders for anomaly detection and feature extraction.

Autoencoders as Neural Network Version of PCA

Diverse Autoencoders and Their Applications

Anomaly Detection with Autoencoders

Building Sparse Autoencoders

Advanced Autoencoders for Sequence and Image Data

Enhancing Autoencoder Models

Optimizing Autoencoder Architectures

Quiz: Autoencoders

Conclusion

1 Lessons

Build on your mastery of deep learning techniques in rare event prediction.

Closing Thoughts

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

Chitta Ranjan

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