This repository is for the book, Understanding Deep Learning: Application in Rare Event Prediction.
The link to the book is here.
Created and maintained by,
Chitta Ranjan, Ph.D.
LinkedIn: www.linkedin.com/in/chitta-ranjan-b0851911/
Medium: https://medium.com/@cran2367
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Introduction
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Rare Event Prediction
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b. Setup data
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Autoencoders
- Download the data from: data link
- Place the data file in the
/data/directory.
The video lectures of the chapter are published on YouTube.
Chapter 3 - Part 1 - TensorFlow Installation - Background
Chapter 3 - Part 2 - Getting Started - TensorFlow on Google Colab
Chapter 3 - Part 3 - Data set for Rare Event Prediction
Chapter 4 - Part 1 - Background
Chapter 4 - Part 2 - Understanding Multilayer Perceptrons
Chapter 4 - Part 3 - Deep Learning and Neural Network
Chapter 4 - Part 4 - Intuition behind Neural Networks
Chapter 4 - Part 5 - MLP Math Operations I
Chapter 4 - Part 5 - MLP Math Operations II
Chapter 4 - Part 6 - Importance of Nonlinear Activation
Chapter 4 - Part 7 - Back Propagation
Chapter 4 - Part 8 - Intermission-Outline of rest of the chapter
Chapter 4 - Part 9 - Curve Shifting for Early Prediction
Chapter 4 - Part 10 - MLP training iteration levels
Chapter 4 - Part 11 - Custom metric in TensorFlow I
Chapter 4 - Part 11 - Custom metric in TensorFlow II
Chapter 4 - Part 12 - Dropout I - Co-adaptation Phenomenon
Chapter 4 - Part 12 - Dropout II - Underlying Concept
Chapter 4 - Part 12 - Dropout III (as) A Regularization Technique
Chapter 4 - Part 13 - Activations I - Importance of Gradients
Chapter 4 - Part 13 - Activations II -Gradient vis-à-vis Learning
Chapter 4 - Part 14 - Vanishing & Exploding Gradient
Chapter 4 - Part 15 - Story of Activation Functions
Chapter 4 - Part 16 - Rules-of-thumb MLP Dense Layer Models
Chapter 4 - Codes I - MLP Modeling - Get ready with Data Scaling
Chapter 4 - Codes II - MLP - First (Baseline) Dense Layer Model
The videos of the next chapters will be published from now until the end of 2021.