Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks - Softcover

Michelucci, Umberto

9781484237892: Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks

Softcover

ISBN 10: 1484237897 ISBN 13: 9781484237892

Verlag: Apress, 2018

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Chapter 1: Introduction

Chapter Goal: Describe the book, the TensorFlow infrastructure, give instructions on how to setup a system for deep learning projects

No of pages : 30-50

Sub -Topics

1. Goal of the book

2. Prerequisites

3. TensorFlow Jupyter Notebooks introduction

4. How to setup a computer to follow the book (docker image?)

5. Tips for TensorFlow development and libraries needed (numpy, matplotlib, etc.)

6. The problem of vectorization of code and calculations

7. Additional resources

Chapter 2: Single Neurons

Chapter Goal: Describe what you can achieve with neural networks with just one neuron.

No of pages: 50-70

Sub -Topics

8. Overview of different parts of a neuron

9. Activation functions (ReLu, sigmoid, modified ReLu, etc.) and their difference (which one is for which task better)

10. The new google activation function SWISH (https://arxiv.org/abs/1710.05941?utm_campaign=Artificial%2BIntelligence%2Band%2BDeep%2BLearning%2BWeekly&utm_medium=email&utm_source=Artificial_Intelligence_and_Deep_Learning_Weekly_35)

11. Optimization algorithm discussion (gradient descent)

12. Linear regression

13. Basic Tensorflow introduction

14. Logistic regression

15. Regression (linear and logistic) with tensorflow

16. Practical case discussed in details

17. The difference between regression and classification for one neuron

18. Tips for TensorFlow implementation

Chapter 3: Fully connected Neural Network with more neurons

Chapter Goal: Describe what is a fully connected neural network and how to implement one (with one or more layers, etc.), and how to perform classification (binary and multi-class and regression)

No of pages: 30-50

Sub -Topics

1. What is a tensor

2. Dimensions of involved tensors (weights, input, etc.) (with tips on TensorFlow implementation)

3. Distinctions between features and labels

4. Problem of initialization of weights (random, constant, zeros, etc.)

5. Second tutorial on tensorflow

6. Practical case discussed in details

7. Tips for TensorFlow implementation

8. Classification and regression with such networks and how the output layer is different

9. Softmax for multi-class classification

10. Binary classification

Chapter 4: Neural networks error analysis

Chapter Goal: Describe the problem of identifying the sources of errors (variance, bias, data skewed, not enough data, overfitting, etc.)

No of pages: 50-70

Sub -Topics

1. Train, dev and test dataset - why do we need three? Do we need four? What can we detect with different datasets and how to use them or size them?

2. Sources of errors (overfitting, bias, variance, etc.)

3. What is overfitting, a discussion

4. Why is overfitting important with neural networks?

5. Practical case discussion

6. A guide on how to perform error analysis

7. A practical example with a complete error analysis

8. The problem of different datasets (train, dev, test, etc.) coming from different distributions

9. Data augmentation techniques and examples

10. How to deal with too few data

11. How to split the datasets (train, dev, test)? Not 60/20/20 but more 98/1/1 when we have a LOT of data.

12. Tips for TensorFlow implementation

Chapter 5: Dropout technique

Chapter Goal: Describe what dropout is, when to employ it

No of pages: 30-50

Sub -Topics

1. What is dropout ?

2. When we need to

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�ber die Autorin bzw. den Autor

Umberto is currently the head of Innovation in BI & Analytics at a leading health insurance company in Switzerland, where he leads several strategic initiatives that deal with AI, new technologies and machine learning. He worked as data scientist and lead modeller in several big projects in healthcare and has extensive hands-on experience in programming and designing algorithms. Before that he managed projects in BI and DWH enabling data driven solutions to be implemented in complicated productive environments. He worked extensively with neural networks the last two years and applied deep learning to several problems linked to insurance and client behaviour (like customer churning). He presented his results on deep learning at international conferences and internally gained a reputation for his huge experience with Python and deep learning.

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Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish), seeing how to perform linear and logistic regression using TensorFlow, and choosing the right cost function. 

The next section talks about more complicated neural network architectures with several layers and neurons and explores the problem of random initialization of weights. An entire chapter is dedicated to a complete overview of neural network error analysis, giving examples of solving problems originating from variance, bias, overfitting, and datasets coming from different distributions. 

Applied Deep Learning also discusses how to implement logistic regression completely from scratch without using any Python library except NumPy, to let you appreciate how libraries such as TensorFlow allow quick and efficient experiments. Case studies for each method are included to put into practice all theoretical information. You ll discover tips and tricks for writing optimized Python code (for example vectorizing loops with NumPy). 

You will:

Implement advanced techniques in the right way in Python and TensorFlow
Debug and optimize advanced methods (such as dropout and regularization)
Carry out error analysis (to realize if one has a bias problem, a variance problem, a data offset problem, and so on)
Set up a machine learning project focused on deep learning on a complex dataset

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