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Название PYTHON MACHINE LEARNING;MACHINE LEARNING AND DEEP LEARNING WITH PYTHON, SCIKIT-LEARN, AND TENSORFLOW 2, 3RD EDITION
Авторы SEBASTIAN RASCHKA;VAHID MIRJALIL I.
Выходные сведения [S.l.]: PACKT PUBLISHING, 2019
Коллекция Электронные книги зарубежных издательств ; Общая коллекция
Тематика Python (Computer program language) ; Machine learning. ; EBSCO eBooks
Тип документа Другой
Тип файла PDF
Язык Английский
Права доступа Доступ по паролю из сети Интернет (чтение, печать, копирование)
Ключ записи on1131778945
Дата создания записи 19.12.2019

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  • Cover
  • Copyright
  • Packt Page
  • Contributors
  • Table of Contents
  • Preface
  • Chapter 1: Giving Computers the Ability to Learn from Data
    • Building intelligent machines to transform data into knowledge
    • The three different types of machine learning
      • Making predictions about the future with supervised learning
        • Classification for predicting class labels
        • Regression for predicting continuous outcomes
      • Solving interactive problems with reinforcement learning
      • Discovering hidden structures with unsupervised learning
        • Finding subgroups with clustering
        • Dimensionality reduction for data compression
    • Introduction to the basic terminology and notations
      • Notation and conventions used in this book
      • Machine learning terminology
    • A roadmap for building machine learning systems
      • Preprocessing – getting data into shape
      • Training and selecting a predictive model
      • Evaluating models and predicting unseen data instances
    • Using Python for machine learning
      • Installing Python and packages from the Python Package Index
      • Using the Anaconda Python distribution and package manager
      • Packages for scientific computing, data science, and machine learning
    • Summary
  • Chapter 2: Training Simple Machine Learning Algorithms for Classification
    • Artificial neurons – a brief glimpse into the early history of machine learning
      • The formal definition of an artificial neuron
      • The perceptron learning rule
    • Implementing a perceptron learning algorithm in Python
      • An object-oriented perceptron API
      • Training a perceptron model on the Iris dataset
    • Adaptive linear neurons and the convergence of learning
      • Minimizing cost functions with gradient descent
      • Implementing Adaline in Python
      • Improving gradient descent through feature scaling
      • Large-scale machine learning and stochastic gradient descent
    • Summary
  • Chapter 3: A Tour of Machine Learning Classifiers Using scikit-learn
    • Choosing a classification algorithm
    • First steps with scikit-learn – training a perceptron
    • Modeling class probabilities via logistic regression
      • Logistic regression and conditional probabilities
      • Learning the weights of the logistic cost function
      • Converting an Adaline implementation into an algorithm for logistic regression
      • Training a logistic regression model with scikit-learn
      • Tackling overfitting via regularization
    • Maximum margin classification with support vector machines
      • Maximum margin intuition
      • Dealing with a nonlinearly separable case using slack variables
      • Alternative implementations in scikit-learn
    • Solving nonlinear problems using a kernel SVM
      • Kernel methods for linearly inseparable data
      • Using the kernel trick to find separating hyperplanes in a high-dimensional space
    • Decision tree learning
      • Maximizing IG – getting the most bang for your buck
      • Building a decision tree
      • Combining multiple decision trees via random forests
    • K-nearest neighbors – a lazy learning algorithm
    • Summary
  • Chapter 4: Building Good Training Datasets – Data Preprocessing
    • Dealing with missing data
      • Identifying missing values in tabular data
      • Eliminating training examples or features with missing values
      • Imputing missing values
      • Understanding the scikit-learn estimator API
    • Handling categorical data
      • Categorical data encoding with pandas
      • Mapping ordinal features
      • Encoding class labels
      • Performing one-hot encoding on nominal features
    • Partitioning a dataset into separate training and test datasets
    • Bringing features onto the same scale
    • Selecting meaningful features
      • L1 and L2 regularization as penalties against model complexity
      • A geometric interpretation of L2 regularization
      • Sparse solutions with L1 regularization
      • Sequential feature selection algorithms
    • Assessing feature importance with random forests
    • Summary
  • Chapter 5: Compressing Data via Dimensionality Reduction
    • Unsupervised dimensionality reduction via principal component analysis
      • The main steps behind principal component analysis
      • Extracting the principal components step by step
      • Total and explained variance
      • Feature transformation
      • Principal component analysis in scikit-learn
    • Supervised data compression via linear discriminant analysis
      • Principal component analysis versus linear discriminant analysis
      • The inner workings of linear discriminant analysis
      • Computing the scatter matrices
      • Selecting linear discriminants for the new feature subspace
      • Projecting examples onto the new feature space
      • LDA via scikit-learn
    • Using kernel principal component analysis for nonlinear mappings
      • Kernel functions and the kernel trick
      • Implementing a kernel principal component analysis in Python
        • Example 1 – separating half-moon shapes
        • Example 2 – separating concentric circles
      • Projecting new data points
      • Kernel principal component analysis in scikit-learn
    • Summary
  • Chapter 6: Learning Best Practices for Model Evaluation and Hyperparameter Tuning
    • Streamlining workflows with pipelines
      • Loading the Breast Cancer Wisconsin dataset
      • Combining transformers and estimators in a pipeline
    • Using k-fold cross-validation to assess model performance
      • The holdout method
      • K-fold cross-validation
    • Debugging algorithms with learning and validation curves
      • Diagnosing bias and variance problems with learning curves
      • Addressing over- and underfitting with validation curves
    • Fine-tuning machine learning models via grid search
      • Tuning hyperparameters via grid search
      • Algorithm selection with nested cross-validation
    • Looking at different performance evaluation metrics
      • Reading a confusion matrix
      • Optimizing the precision and recall of a classification model
      • Plotting a receiver operating characteristic
      • Scoring metrics for multiclass classification
      • Dealing with class imbalance
    • Summary
  • Chapter 7: Combining Different Models for Ensemble Learning
    • Learning with ensembles
    • Combining classifiers via majority vote
      • Implementing a simple majority vote classifier
      • Using the majority voting principle to make predictions
      • Evaluating and tuning the ensemble classifier
    • Bagging – building an ensemble of classifiers from bootstrap samples
      • Bagging in a nutshell
      • Applying bagging to classify examples in the Wine dataset
    • Leveraging weak learners via adaptive boosting
      • How boosting works
      • Applying AdaBoost using scikit-learn
    • Summary
  • Chapter 8: Applying Machine Learning to Sentiment Analysis
    • Preparing the IMDb movie review data for text processing
      • Obtaining the movie review dataset
      • Preprocessing the movie dataset into a more convenient format
    • Introducing the bag-of-words model
      • Transforming words into feature vectors
      • Assessing word relevancy via term frequency-inverse document frequency
      • Cleaning text data
      • Processing documents into tokens
    • Training a logistic regression model for document classification
    • Working with bigger data – online algorithms and out-of-core learning
    • Topic modeling with Latent Dirichlet Allocation
      • Decomposing text documents with LDA
      • LDA with scikit-learn
    • Summary
  • Chapter 9: Embedding a Machine Learning Model into a Web Application
    • Serializing fitted scikit-learn estimators
    • Setting up an SQLite database for data storage
    • Developing a web application with Flask
      • Our first Flask web application
      • Form validation and rendering
        • Setting up the directory structure
        • Implementing a macro using the Jinja2 templating engine
        • Adding style via CSS
        • Creating the result page
    • Turning the movie review classifier into a web application
      • Files and folders – looking at the directory tree
      • Implementing the main application as app.py
      • Setting up the review form
      • Creating a results page template
    • Deploying the web application to a public server
      • Creating a PythonAnywhere account
      • Uploading the movie classifier application
      • Updating the movie classifier
    • Summary
  • Chapter 10: Predicting Continuous Target Variables with Regression Analysis
    • Introducing linear regression
      • Simple linear regression
      • Multiple linear regression
    • Exploring the Housing dataset
      • Loading the Housing dataset into a data frame
      • Visualizing the important characteristics of a dataset
      • Looking at relationships using a correlation matrix
    • Implementing an ordinary least squares linear regression model
      • Solving regression for regression parameters with gradient descent
      • Estimating the coefficient of a regression model via scikit-learn
    • Fitting a robust regression model using RANSAC
    • Evaluating the performance of linear regression models
    • Using regularized methods for regression
    • Turning a linear regression model into a curve – polynomial regression
      • Adding polynomial terms using scikit-learn
      • Modeling nonlinear relationships in the Housing dataset
    • Dealing with nonlinear relationships using random forests
      • Decision tree regression
      • Random forest regression
    • Summary
  • Chapter 11: Working with Unlabeled Data – Clustering Analysis
    • Grouping objects by similarity using k-means
      • K-means clustering using scikit-learn
      • A smarter way of placing the initial cluster centroids using k-means++
      • Hard versus soft clustering
      • Using the elbow method to find the optimal number of clusters
      • Quantifying the quality of clustering via silhouette plots
    • Organizing clusters as a hierarchical tree
      • Grouping clusters in bottom-up fashion
      • Performing hierarchical clustering on a distance matrix
      • Attaching dendrograms to a heat map
      • Applying agglomerative clustering via scikit-learn
    • Locating regions of high density via DBSCAN
    • Summary
  • Chapter 12: Implementing a Multilayer Artificial Neural Network from Scratch
    • Modeling complex functions with artificial neural networks
      • Single-layer neural network recap
      • Introducing the multilayer neural network architecture
      • Activating a neural network via forward propagation
    • Classifying handwritten digits
      • Obtaining and preparing the MNIST dataset
      • Implementing a multilayer perceptron
    • Training an artificial neural network
      • Computing the logistic cost function
      • Developing your understanding of backpropagation
      • Training neural networks via backpropagation
    • About the convergence in neural networks
    • A few last words about the neural network implementation
    • Summary
  • Chapter 13: Parallelizing Neural Network Training with TensorFlow
    • TensorFlow and training performance
      • Performance challenges
      • What is TensorFlow?
      • How we will learn TensorFlow
    • First steps with TensorFlow
      • Installing TensorFlow
      • Creating tensors in TensorFlow
      • Manipulating the data type and shape of a tensor
      • Applying mathematical operations to tensors
      • Split, stack, and concatenate tensors
    • Building input pipelines using tf.data – the TensorFlow Dataset API
      • Creating a TensorFlow Dataset from existing tensors
      • Combining two tensors into a joint dataset
      • Shuffle, batch, and repeat
      • Creating a dataset from files on your local storage disk
      • Fetching available datasets from the tensorflow_datasets library
    • Building an NN model in TensorFlow
      • The TensorFlow Keras API (tf.keras)
      • Building a linear regression model
      • Model training via the .compile() and .fit() methods
      • Building a multilayer perceptron for classifying flowers in the Iris dataset
      • Evaluating the trained model on the test dataset
      • Saving and reloading the trained model
    • Choosing activation functions for multilayer neural networks
      • Logistic function recap
      • Estimating class probabilities in multiclass classification via the softmax function
      • Broadening the output spectrum using a hyperbolic tangent
      • Rectified linear unit activation
    • Summary
  • Chapter 14: Going Deeper – The Mechanics of TensorFlow
    • The key features of TensorFlow
    • TensorFlow's computation graphs: migrating to TensorFlow v2
      • Understanding computation graphs
      • Creating a graph in TensorFlow v1.x
      • Migrating a graph to TensorFlow v2
      • Loading input data into a model: TensorFlow v1.x style
      • Loading input data into a model: TensorFlow v2 style
      • Improving computational performance with function decorators
    • TensorFlow Variable objects for storing and updating model parameters
    • Computing gradients via automatic differentiation and GradientTape
      • Computing the gradients of the loss with respect to trainable variables
      • Computing gradients with respect to non-trainable tensors
      • Keeping resources for multiple gradient computations
    • Simplifying implementations of common architectures via the Keras API
      • Solving an XOR classification problem
      • Making model building more flexible with Keras' functional API
      • Implementing models based on Keras' Model class
      • Writing custom Keras layers
    • TensorFlow Estimators
      • Working with feature columns
      • Machine learning with pre-made Estimators
      • Using Estimators for MNIST handwritten digit classification
      • Creating a custom Estimator from an existing Keras model
    • Summary
  • Chapter 15: Classifying Images with Deep Convolutional Neural Networks
    • The building blocks of CNNs
      • Understanding CNNs and feature hierarchies
      • Performing discrete convolutions
        • Discrete convolutions in one dimension
        • Padding inputs to control the size of the output feature maps
        • Determining the size of the convolution output
        • Performing a discrete convolution in 2D
      • Subsampling layers
    • Putting everything together – implementing a CNN
      • Working with multiple input or color channels
      • Regularizing an NN with dropout
      • Loss functions for classification
    • Implementing a deep CNN using TensorFlow
      • The multilayer CNN architecture
      • Loading and preprocessing the data
      • Implementing a CNN using the TensorFlow Keras API
        • Configuring CNN layers in Keras
        • Constructing a CNN in Keras
    • Gender classification from face images using a CNN
      • Loading the CelebA dataset
      • Image transformation and data augmentation
      • Training a CNN gender classifier
    • Summary
  • Chapter 16: Modeling Sequential Data Using Recurrent Neural Networks
    • Introducing sequential data
      • Modeling sequential data—order matters
      • Representing sequences
      • The different categories of sequence modeling
    • RNNs for modeling sequences
      • Understanding the RNN looping mechanism
      • Computing activations in an RNN
      • Hidden-recurrence versus output-recurrence
      • The challenges of learning long-range interactions
      • Long short-term memory cells
    • Implementing RNNs for sequence modeling in TensorFlow
      • Project one: predicting the sentiment of IMDb movie reviews
        • Preparing the movie review data
        • Embedding layers for sentence encoding
        • Building an RNN model
        • Building an RNN model for the sentiment analysis task
      • Project two: character-level language modeling in TensorFlow
        • Preprocessing the dataset
        • Building a character-level RNN model
        • Evaluation phase: generating new text passages
    • Understanding language with the Transformer model
      • Understanding the self-attention mechanism
        • A basic version of self-attention
        • Parameterizing the self-attention mechanism with query, key, and value weights
      • Multi-head attention and the Transformer block
    • Summary
  • Chapter 17: Generative Adversarial Networks for Synthesizing New Data
    • Introducing generative adversarial networks
      • Starting with autoencoders
      • Generative models for synthesizing new data
      • Generating new samples with GANs
      • Understanding the loss functions of the generator and discriminator networks in a GAN model
    • Implementing a GAN from scratch
      • Training GAN models on Google Colab
      • Implementing the generator and the discriminator networks
      • Defining the training dataset
      • Training the GAN model
    • Improving the quality of synthesized images using a convolutional and Wasserstein GAN
      • Transposed convolution
      • Batch normalization
      • Implementing the generator and discriminator
      • Dissimilarity measures between two distributions
      • Using EM distance in practice for GANs
      • Gradient penalty
      • Implementing WGAN-GP to train the DCGAN model
      • Mode collapse
    • Other GAN applications
    • Summary
  • Chapter 18: Reinforcement Learning for Decision Making in Complex Environments
    • Introduction: learning from experience
      • Understanding reinforcement learning
      • Defining the agent-environment interface of a reinforcement learning system
    • The theoretical foundations of RL
      • Markov decision processes
      • The mathematical formulation of Markov decision processes
        • Visualization of a Markov process
        • Episodic versus continuing tasks
      • RL terminology: return, policy, and value function
        • The return
        • Policy
        • Value function
      • Dynamic programming using the Bellman equation
    • Reinforcement learning algorithms
      • Dynamic programming
        • Policy evaluation – predicting the value function with dynamic programming
        • Improving the policy using the estimated value function
        • Policy iteration
        • Value iteration
      • Reinforcement learning with Monte Carlo
        • State-value function estimation using MC
        • Action-value function estimation using MC
        • Finding an optimal policy using MC control
        • Policy improvement – computing the greedy policy from the action-value function
      • Temporal difference learning
        • TD prediction
        • On-policy TD control (SARSA)
        • Off-policy TD control (Q-learning)
    • Implementing our first RL algorithm
      • Introducing the OpenAI Gym toolkit
        • Working with the existing environments in OpenAI Gym
        • A grid world example
        • Implementing the grid world environment in OpenAI Gym
      • Solving the grid world problem with Q-learning
        • Implementing the Q-learning algorithm
      • A glance at deep Q-learning
        • Training a DQN model according to the Q-learning algorithm
        • Implementing a deep Q-learning algorithm
    • Chapter and book summary
  • Other Books You May Enjoy
  • Index
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