Machine Learning for Developers by Bonnin, Rodolfo -- Read -- Imperial Library of Trantor

Index

Title Page Copyright

Machine Learning for Developers

Credits Foreword About the Author About the Reviewers www.PacktPub.com

Why subscribe?

Customer Feedback Preface

What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support

Downloading the example code Errata Piracy Questions

Introduction - Machine Learning and Statistical Science

Machine learning in the bigger picture

Types of machine learning

Grades of supervision Supervised learning strategies - regression versus classification Unsupervised problem solving–clustering

Tools of the trade–programming language and libraries

The Python language

The NumPy library The matplotlib library

What's matplotlib?

Pandas SciPy Jupyter notebook

Basic mathematical concepts

Statistics - the basic pillar of modeling uncertainty

Descriptive statistics - main operations

Mean Variance Standard deviation

Probability and random variables

Events

Probability Random variables and distributions

Useful probability distributions

Bernoulli distributions Uniform distribution Normal distribution Logistic distribution

Statistical measures for probability functions

Skewness Kurtosis

Differential calculus elements Preliminary knowledge

In search of changes–derivatives

Sliding on the slope

Chain rule

Partial derivatives

Summary

The Learning Process

Understanding the problem Dataset definition and retrieval

The ETL process Loading datasets and doing exploratory analysis with SciPy and pandas Working interactively with IPython Working on 2D data

Feature engineering

Imputation of missing data One hot encoding

Dataset preprocessing

Normalization and feature scaling

Normalization or standardization

Model definition

Asking ourselves the right questions

Loss function definition Model fitting and evaluation

Dataset partitioning

Common training terms – iteration, batch, and epoch Types of training – online and batch processing Parameter initialization

Model implementation and results interpretation

Regression metrics

Mean absolute error Median absolute error Mean squared error

Classification metrics

Accuracy Precision score, recall, and F-measure Confusion matrix

Clustering quality measurements

Silhouette coefficient Homogeneity, completeness, and V-measure

Summary References

Clustering

Grouping as a human activity Automating the clustering process Finding a common center - K-means

Pros and cons of K-means K-means algorithm breakdown K-means implementations

Nearest neighbors

Mechanics of K-NN

Pros and cons of K-NN

K-NN sample implementation

Going beyond the basics

The Elbow method

Summary References

Linear and Logistic Regression

Regression analysis

Applications of regression

Quantitative versus qualitative variables

Linear regression

Determination of the cost function

The many ways of minimizing errors

Analytical approach

Pros and cons of the analytical approach

Covariance/correlation method

Covariance Correlation

Searching for the slope and intercept with covariance and correlation Gradient descent

Some intuitive background The gradient descent loop

Formalizing our concepts

Expressing recursion as a process

Going practical – new tools for new methods Useful diagrams for variable explorations – pairplot Correlation plot

Data exploration and linear regression in practice

The Iris dataset

Getting an intuitive idea with Seaborn pairplot Creating the prediction function Defining the error function Correlation fit Polynomial regression and an introduction to underfitting and overfitting

Linear regression with gradient descent in practice

Logistic regression

Problem domain of linear regression and logistic regression

Logistic function predecessor – the logit functions Link function

Logit function

Logit function properties The importance of the logit inverse The sigmoid or logistic function Properties of the logistic function Multiclass application – softmax regression

Practical example – cardiac disease modeling with logistic regression

The CHDAGE dataset Dataset format

Summary References

Neural Networks

History of neural models

The perceptron model Improving our predictions – the ADALINE algorithm Similarities and differences between a perceptron and ADALINE

Limitations of early models

Single and multilayer perceptrons

MLP origins The feedforward mechanism The chosen optimization algorithm – backpropagation

Types of problem to be tackled

Implementing a simple function with a single-layer perceptron

Defining and graphing transfer function types Representing and understanding the transfer functions Sigmoid or logistic function Playing with the sigmoid Rectified linear unit or ReLU Linear transfer function Defining loss functions for neural networks

L1 versus L2 properties

Summary References

Convolutional Neural Networks

Origin of convolutional neural networks

Getting started with convolution

Continuous convolution Discrete convolution

Kernels and convolutions

Stride and padding

Implementing the 2D discrete convolution operation in an example Subsampling operation (pooling) Improving efficiency with the dropout operation

Advantages of the dropout layers

Deep neural networks

Deep convolutional network architectures through time

Lenet 5 Alexnet The VGG model GoogLenet and the Inception model

Batch-normalized inception V2 and V3

Residual Networks (ResNet)

Types of problem solved by deep layers of CNNs

Classification Detection Segmentation

Deploying a deep neural network with Keras Exploring a convolutional model with Quiver

Exploring a convolutional network with Quiver Implementing transfer learning

References Summary

Recurrent Neural Networks

Solving problems with order — RNNs

RNN definition

Types of sequence to be modeled

Development of RNN

Training method — backpropagation through time Main problems of the traditional RNNs — exploding and vanishing gradients

LSTM

The gate and multiplier operation Part 1 — set values to forget (input gate) Part 2 — set values to keep Part 3 — apply changes to cell Part 4 — output filtered cell state

Univariate time series prediction with energy consumption data

Dataset description and loading

Dataset preprocessing

Summary References

Recent Models and Developments

GANs

Types of GAN applications

Discriminative and generative models

Reinforcement learning

Markov decision process

Decision elements

Optimizing the Markov process

Basic RL techniques: Q-learning References Summary

Software Installation and Configuration

Linux installation

Initial distribution requirements Installing Anaconda on Linux pip Linux installation method

Installing the Python 3 interpreter Installing pip Installing necessary libraries

macOS X environment installation

Anaconda installation Installing pip

Installing remaining libraries via pip

Windows installation

Anaconda Windows installation

Summary

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