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Machine Learning for Developers

更新時間：2021-07-02 15:47:29

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ThisbookwillappealtoanydeveloperwhowantstoknowwhatMachineLearningisandiskeentouseMachineLearningtomaketheirday-to-dayappsfast,highperforming,andaccurate.AnydeveloperwhowantstoenterthefieldofMachineLearningcaneffectivelyusethisbookasanentrypoint.

目錄(250章)

倒序

coverpage
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 更新時間：2021-07-02 15:47:29

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Machine Learning for Developers