Scala for Machine Learning(2nd) 无水印pdf

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Table of contents cala for Machine Learning Second Edition Credits About the author about the reviewers www.packtpub.com e. discount offers and more 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 Downloading the color images of this book Errata Pj ac uestions 1. Getting Started Mathematical notations for the curious Why machine learning? Classification Prediction Optimization Regression Why scala? Scala as a functional language Abstraction Higher kinded types Functors one Scala as an object oriented language Scala as a scalable language Model categorization Taxonomy of machine learning algorithms 丿 supervised learning Clustering Dimension reduction Supervised learning Generative models Discriminative models Semi-supervised learning Reinforcement learning leveraging Java libraries Tools and frameworks Java Scala Eclipse Scala IDE Intelli. J IDEA Scala plugin Simple build tool Apache Commons Math Description Licensing Installation JFree Chart Description Licensing Installation Other libraries and frameworks Source code Convention Context bounds Presentation Primitives and implicits Immutability Let's kick the tires Writing a simple workflow Step 1-scoping the problem Step 2-loading data Step. -preprocessing data Immutable normalization Step 4-discovering patterns Analyzing data Plotting data Visualizing model features Ⅴ isualizing labe Step 5-implementing the e class fi er Selecting an optimizer Training the model Classifying observations tep 6-evaluating the model summary 2. Data Pipelines Modeling What is a model? Model versus design Selecting features Extracting features Defining a methodolo Monadic data transformation Error handling Monads to the rescue Implicit models Explicit models Workflow computational model upporting mathematical abstractions Step 1-variable declaration Step 2- model definition Step 3-instantiation Composing mixins to build workflow Understanding the problem Defining modules Instantiating the workflow Modularizing Profiling data Immutable statistics Z-score and gauss Assessing a model Validation Key quality metrics F-score for binomial classification f-score for multinomial classification Area under the curves Area under prc Area under roc Cross-validation One-fold cross-validation K-fold cross-validation Blas-varlance decomposition Overfitting Summary 3. Data Preprocessing Time series in scala Context bounds Types and operations Transpose operator Differential operator Lazy views Moving averages Simple moving average Weighted moving average Exponential moving average Fourier analysis Discrete Fourier transform DFT) DFT-based filtering Detection of market cycles The discrete Kalman filter The state space estimation The transition equation The measurement equation The recursive algorithm Prediction Correction Kalman smoothing Fixed lag smoothing Experimentation Benefits and drawbacks Alternative preprocessing techniques Summary 4. Unsupervised Learning K-mean clustering K-means Measuring similarity Defining the algorithm Step 1-Clusters configuration Defining clusters Initializing clusters Step 2-Clusters assignment Step 3-Reconstruction error minimization Creating K-means components recursive implementation Iterative implementation Step 4-Classification Curse of dimensionality Evaluation The results Tuning the number of clusters Validation Expectation-Maximization(EM) Gaussian mixture model EMoverview Implementation Classification Testing Online em Summary 5. Dimension reduction Challenging model complexity The divergences The Kullback-Leibler divergence Overview Implementation Testing The mutual information Principal components analysis (pca Algorithm Implementation Test case Evaluation Extending PCa Validation Categorical features Performance Nonlinear models Kernel pca Manifolds Summary 6. Naive Bayes Classifiers Probabilistic graphical models Naive Bayes classifiers Introducing the multinomial Naive bayes Formalism The frequentist perspective The predictive model he zero-frequency problem Implementation Design raining Class likelihood Binomial model Multinomial model Classifier components Classification F1 Validation Features extraction Testing Multivariate bernoulli classification Model Implementation Naive Bayes and text mining Basics information retrieval Implementation analyzing documents Extracting relative terms frequency Generating the features Testing Retrieving textual information Evaluating text mining classifier Pros and cons Summary Sequential Data Models Markov decision processes The Markov property The first-order discrete markov chain The hidden Markov model (HMM) Notation The lambda model Design Evaluation(CF-1 Alpha forward pass Beta(backward pass Training(CF-2 Baum-Welch estimator (EM Decoding ( cF-3 The viterbi algorithm Putting it all together Test case 1-1 Training Test case 2- Evaluation HMM as filtering technique Conditional random fields Introduction to CrF inear chain cre Regularized crf and text analytics The feature functions model Design mplementation Configuring the CrF classifier Training the Cre model applying the cre model Tests The training convergence profile Impact of the size of the training set Impact of L2 regularization factor Comparing crf and HMM Performance consideration Summary 8. Monte carlo inference The purpose of sampling Gaussian sampling Boⅹ- Muller transform onte Carlo approximation Overview Implementation Bootstrapping with replacement Overview Resampling Implementation Pros and cons of bootstrap Markov Chain Monte Carlo(MCMC) Overview Metropolis-HastingS( ME Implementation Test Summary 9. Regression and Regularization Inear regression Univariate linear regression Implementation est case 【实例截图】
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