{"product_id":"139613","title":"Machine Learning Textbook: PyTorch Edition ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jXPBvkf6hwzomkAtJ3Gt6Fu24vO2N.png?v=1765075558\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e Machine Learning Textbook: PyTorch Edition \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/123802975\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e \u003cb\u003eMeet Amazon's best-selling books in PyTorch version!\u003cbr\u003e A practical guide to machine learning\/deep learning with solid theory and diverse examples.\u003cbr\u003e\u003c\/b\u003e \u003cbr\u003eThe Amazon bestseller \"Machine Learning Textbook\" has been reborn with a PyTorch edition! This book provides a balanced explanation of theory and code, covering the concepts necessary to truly understand machine learning and deep learning, how core algorithms work and how to use them, the underlying mathematics, practical examples, and how to avoid common pitfalls.\u003cbr\u003e Additionally, machine learning is explained using Python-based core libraries (SciPy, NumPy, scikit-learn, Matplotlib, Pandas), and deep learning is explained using PyTorch.\u003cbr\u003e In addition to the core concepts of PyTorch, it also covers the latest trends such as transformers, PyTorch Lightning, XGBoost, and graph neural networks in addition to the contents covered in the 3rd edition of the Machine Learning Textbook. It is based on the latest versions of both scikit-learn and PyTorch.\u003cbr\u003e Recommended for those who want to build a solid foundation in machine learning and deep learning.\u003cbr\u003e\n\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents. \u003cbr\u003e\u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eChapter 1.\u003cbr\u003e Computers learn from data\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 1.1 Building an intelligent system that turns data into knowledge\u003cbr\u003e 1.2 Three Types of Machine Learning\u003cbr\u003e __1.2.1 Predicting the Future with Supervised Learning\u003cbr\u003e __1.2.2 Solving Reactive Problems with Reinforcement Learning\u003cbr\u003e __1.2.3 Discovering Hidden Structure with Unsupervised Learning\u003cbr\u003e 1.3 Introduction to basic terminology and notation\u003cbr\u003e __1.3.1 Notation and conventions used in this book\u003cbr\u003e __1.3.2 Machine Learning Terminology\u003cbr\u003e 1.4 Machine Learning System Building Roadmap\u003cbr\u003e __1.4.1 Preprocessing: Shaping the Data\u003cbr\u003e __1.4.2 Training and selecting a predictive model\u003cbr\u003e __1.4.3 Evaluate the model and predict with unseen samples\u003cbr\u003e 1.5 Python for Machine Learning\u003cbr\u003e __1.5.1 Installing packages with Python and PIP\u003cbr\u003e __1.5.2 Using the Anaconda Python distribution and package manager\u003cbr\u003e __1.5.3 Packages for scientific computing, data science, and machine learning\u003cbr\u003e 1.6 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 2.\u003cbr\u003e Training a simple classification algorithm\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 2.1 Artificial Neurons: A Brief History of Early Machine Learning \u003cbr\u003e__2.1.1 Mathematical Definition of Artificial Neurons\u003cbr\u003e __2.1.2 Perceptron learning rules\u003cbr\u003e 2.2 Implementing the Perceptron Learning Algorithm in Python\u003cbr\u003e __2.2.1 Object-Oriented Perceptron API\u003cbr\u003e __2.2.2 Training a perceptron on the iris dataset\u003cbr\u003e 2.3 Adaptive linear neurons and learning convergence\u003cbr\u003e __2.3.1 Minimizing the loss function using gradient descent\u003cbr\u003e __2.3.2 Implementing Adalin in Python\u003cbr\u003e __2.3.3 Improving Gradient Descent Results by Adjusting Feature Scales\u003cbr\u003e __2.3.4 Large-Scale Machine Learning and Stochastic Gradient Descent\u003cbr\u003e 2.4 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 3.\u003cbr\u003e A Tour of Machine Learning Classification Models with Scikit-Learn\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 3.1 Choosing a Classification Algorithm\u003cbr\u003e 3.2 First Steps with Scikit-Learn: Training a Perceptron\u003cbr\u003e 3.3 Class Probability Modeling Using Logistic Regression\u003cbr\u003e __3.3.1 Understanding Logistic Regression and Conditional Probability\u003cbr\u003e __3.3.2 Learning weights for the logistic loss function\u003cbr\u003e __3.3.3 Change the Adalin implementation to a logistic regression algorithm\u003cbr\u003e __3.3.4 Training a Logistic Regression Model Using Scikit-Learn \u003cbr\u003e__3.3.5 Avoiding Overfitting Using Regularization\u003cbr\u003e 3.4 Maximum Margin Classification Using Support Vector Machines\u003cbr\u003e __3.4.1 Maximum Margin\u003cbr\u003e __3.4.2 Tackling Nonlinear Classification Problems Using Slack Variables\u003cbr\u003e __3.4.3 Other implementations of scikit-learn\u003cbr\u003e 3.5 Solving Nonlinear Problems Using Kernel SVMs\u003cbr\u003e __3.5.1 Kernel methods for linearly non-separable data\u003cbr\u003e __3.5.2 Finding a Partitioning Hyperplane in High-Dimensional Space Using Kernel Techniques\u003cbr\u003e 3.6 Decision Tree Learning\u003cbr\u003e __3.6.1 Maximizing Information Gains: Maximizing Resource Utilization\u003cbr\u003e __3.6.2 Creating a decision tree\u003cbr\u003e __3.6.3 Connecting Multiple Decision Trees with Random Forests\u003cbr\u003e 3.7 k-nearest neighbors: a lazy learning algorithm\u003cbr\u003e 3.8 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 4.\u003cbr\u003e Creating a Good Training Dataset: Data Preprocessing\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 4.1 Handling Missing Data\u003cbr\u003e __4.1.1 Identifying missing values ​​in tabular data\u003cbr\u003e __4.1.2 Excluding training samples or features with missing values\u003cbr\u003e __4.1.3 Replacing missing values\u003cbr\u003e __4.1.4 Getting familiar with the scikit-learn estimator API \u003cbr\u003e4.2 Handling Categorical Data\u003cbr\u003e __4.2.1 Encoding Categorical Data Using Pandas\u003cbr\u003e __4.2.2 Ordered feature mapping\u003cbr\u003e __4.2.3 Class Label Encoding\u003cbr\u003e __4.2.4 Applying one-hot encoding to unordered features\u003cbr\u003e 4.3 Divide the dataset into training and test datasets.\u003cbr\u003e 4.4 Matching the trait scale\u003cbr\u003e 4.5 Choosing Useful Traits\u003cbr\u003e __4.5.1 L1 regularization and L2 regularization for limiting model complexity\u003cbr\u003e __4.5.2 Geometric interpretation of L2 regularization\u003cbr\u003e __4.5.3 Sparsity using L1 regularization\u003cbr\u003e __4.5.4 Sequential Feature Selection Algorithm\u003cbr\u003e 4.6 Using feature importance in random forests\u003cbr\u003e 4.7 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 5.\u003cbr\u003e Data compression using dimensionality reduction\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 5.1 Unsupervised dimensionality reduction using principal component analysis\u003cbr\u003e __5.1.1 Key steps of principal component analysis\u003cbr\u003e __5.1.2 Principal component extraction step\u003cbr\u003e __5.1.3 Total variance and explained variance\u003cbr\u003e __5.1.4 Attribute Conversion\u003cbr\u003e __5.1.5 Principal Component Analysis in Scikit-Learn\u003cbr\u003e 5.2 Supervised data compression using linear discriminant analysis\u003cbr\u003e __5.2.1 Principal Component Analysis vs. Linear Discriminant Analysis \u003cbr\u003e__5.2.2 How Linear Discriminant Analysis Works Internally\u003cbr\u003e __5.2.3 Calculating the Scatter Matrix\u003cbr\u003e __5.2.4 Selecting linear discriminant vectors for new feature subspaces\u003cbr\u003e __5.2.5 Projecting samples into a new feature space\u003cbr\u003e __5.2.6 LDA in scikit-learn\u003cbr\u003e 5.3 Nonlinear dimensionality reduction and visualization\u003cbr\u003e __5.3.1 Why consider nonlinear dimensionality reduction?\u003cbr\u003e __5.3.2 Data Visualization Using t-SNE\u003cbr\u003e 5.4 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 6.\u003cbr\u003e Best Practices for Model Evaluation and Hyperparameter Tuning\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 6.1 Efficient Workflow Using Pipelines\u003cbr\u003e __6.1.1 Wisconsin Breast Cancer Dataset\u003cbr\u003e __6.1.2 Connecting transformers and estimators with pipelines\u003cbr\u003e 6.2 Evaluating Model Performance Using k-Fold Cross-Validation\u003cbr\u003e __6.2.1 Holdout Method\u003cbr\u003e __6.2.2 k-fold cross-validation\u003cbr\u003e 6.3 Debugging Algorithms Using Learning and Validation Curves\u003cbr\u003e __6.3.1 Analyzing Bias and Variance Problems with Learning Curves\u003cbr\u003e __6.3.2 Investigating overfitting and underfitting with validation curves\u003cbr\u003e 6.4 Fine-tuning machine learning models using grid search \u003cbr\u003e__6.4.1 Hyperparameter Tuning Using Grid Search\u003cbr\u003e __6.4.2 Exploring Hyperparameter Settings More Broadly with Random Search\u003cbr\u003e __6.4.3 Resource-Efficient Hyperparameter Search Using the SH Method\u003cbr\u003e __6.4.4 Algorithm Selection Using Nested Cross-Validation\u003cbr\u003e 6.5 Various performance evaluation indicators\u003cbr\u003e __6.5.1 Error matrix\u003cbr\u003e __6.5.2 Optimizing the precision and recall of classification models\u003cbr\u003e __6.5.3 Drawing the ROC curve\u003cbr\u003e __6.5.4 Performance metrics for multi-classification\u003cbr\u003e __6.5.5 Handling Unbalanced Classes\u003cbr\u003e 6.6 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 7.\u003cbr\u003e Ensemble learning combining different models\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 7.1 Ensemble Learning\u003cbr\u003e 7.2 Classification ensemble using majority voting\u003cbr\u003e __7.2.1 Implementing a Simple Majority Voting Classifier\u003cbr\u003e __7.2.2 Making Predictions Using Majority Voting\u003cbr\u003e __7.2.3 Evaluation and Tuning of Ensemble Classifiers\u003cbr\u003e 7.3 Bagging: Classification Ensembles via Bootstrap Sampling\u003cbr\u003e __7.3.1 How the Bagging Algorithm Works\u003cbr\u003e __7.3.2 Classifying Samples of the Wine Dataset Using Bagging\u003cbr\u003e 7.4 AdaBoost with Weak Learners \u003cbr\u003e__7.4.1 How Boosting Works\u003cbr\u003e __7.4.2 Using AdaBoost in Scikit-learn\u003cbr\u003e 7.5 Gradient Boosting: Loss Gradient-Based Ensemble Training\u003cbr\u003e __7.5.1 Comparison of AdaBoost and Gradient Boosting\u003cbr\u003e __7.5.2 Introduction to the Gradient Boosting Algorithm\u003cbr\u003e __7.5.3 Gradient Boosting Algorithm for Classification\u003cbr\u003e __7.5.4 Gradient Boosting Classification Example\u003cbr\u003e __7.5.5 Using XGBoost\u003cbr\u003e 7.6 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 8.\u003cbr\u003e Applying Machine Learning to Sentiment Analysis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 8.1 Preparing IMDb Movie Review Data for Text Processing\u003cbr\u003e __8.1.1 Obtaining a movie review dataset\u003cbr\u003e __8.1.2 Preprocessing the movie review dataset into a simpler form\u003cbr\u003e 8.2 Introduction to the BoW Model\u003cbr\u003e __8.2.1 Converting words into feature vectors\u003cbr\u003e __8.2.2 Evaluating word relevance using tf-idf\u003cbr\u003e __8.2.3 Text Data Cleaning\u003cbr\u003e __8.2.4 Splitting a document into tokens\u003cbr\u003e 8.3 Training a Logistic Regression Model for Document Classification\u003cbr\u003e 8.4 Large-Scale Data Processing: Online Algorithms and External Memory Learning \u003cbr\u003e8.5 Topic Modeling Using Latent Dirichlet Allocation\u003cbr\u003e __8.5.1 Text Document Decomposition Using LDA\u003cbr\u003e __8.5.2 LDA in scikit-learn\u003cbr\u003e 8.6 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 9.\u003cbr\u003e Predicting continuous target variables using regression analysis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 9.1 Linear Regression\u003cbr\u003e __9.1.1 Simple linear regression\u003cbr\u003e __9.1.2 Multiple Linear Regression\u003cbr\u003e 9.2 Exploring the Ames Housing Dataset\u003cbr\u003e __9.2.1 Reading the Ames Housing Dataset as a DataFrame\u003cbr\u003e __9.2.2 Visualizing Key Features of the Dataset\u003cbr\u003e __9.2.3 Analysis using the correlation matrix\u003cbr\u003e 9.3 Implementing a Least Squares Linear Regression Model\u003cbr\u003e __9.3.1 Finding the parameters of a regression model using gradient descent\u003cbr\u003e __9.3.2 Estimating the weights of a regression model with scikit-learn\u003cbr\u003e 9.4 Training a Stable Regression Model Using RANSAC\u003cbr\u003e 9.5 Performance Evaluation of Linear Regression Models\u003cbr\u003e 9.6 Applying regularization to regression\u003cbr\u003e 9.7 Converting a linear regression model to polynomial regression\u003cbr\u003e __9.7.1 Adding Polynomial Terms Using Scikit-learn\u003cbr\u003e __9.7.2 Modeling Nonlinear Relationships Using the Ames Housing Dataset \u003cbr\u003e9.8 Handling Nonlinear Relationships Using Random Forests\u003cbr\u003e __9.8.1 Decision Tree Regression\u003cbr\u003e __9.8.2 Random Forest Regression\u003cbr\u003e 9.9 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 10.\u003cbr\u003e Handling Unlabeled Data: Cluster Analysis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 10.1 Grouping similar objects using the k-means algorithm\u003cbr\u003e __10.1.1 k-means clustering using scikit-learn\u003cbr\u003e __10.1.2 Smartly assigning initial cluster centroids with k-means++\u003cbr\u003e __10.1.3 Direct vs. Indirect Clustering\u003cbr\u003e __10.1.4 Finding the optimal number of clusters using the elbow method\u003cbr\u003e __10.1.5 Quantifying cluster quality with silhouette graphs\u003cbr\u003e 10.2 Organizing clusters into a hierarchical tree\u003cbr\u003e __10.2.1 Clustering Bottom-Up\u003cbr\u003e __10.2.2 Performing hierarchical clustering on a distance matrix\u003cbr\u003e __10.2.3 Connecting a dendrogram to a heatmap\u003cbr\u003e __10.2.4 Applying merge clustering in scikit-learn\u003cbr\u003e 10.3 Finding dense areas using DBSCAN\u003cbr\u003e 10.4 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 11.\u003cbr\u003e Implementing a multilayer artificial neural network from scratch\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 11.1 Modeling Complex Functions with Artificial Neural Networks \u003cbr\u003e__11.1.1 Summary of Single-Layer Neural Networks\u003cbr\u003e __11.1.2 Multilayer neural network structure\u003cbr\u003e __11.1.3 Calculating neural network activation output using forward computation\u003cbr\u003e 11.2 Handwritten Number Classification\u003cbr\u003e __11.2.1 Obtaining the MNIST dataset\u003cbr\u003e __11.2.2 Implementation of a multilayer perceptron\u003cbr\u003e __11.2.3 Coding the Neural Network Training Loop\u003cbr\u003e __11.2.4 Performance Evaluation of Neural Network Models\u003cbr\u003e 11.3 Training artificial neural networks\u003cbr\u003e __11.3.1 Calculating the loss function\u003cbr\u003e __11.3.2 Understanding the Backpropagation Algorithm\u003cbr\u003e __11.3.3 Training a Neural Network with the Backpropagation Algorithm\u003cbr\u003e 11.4 Convergence of Neural Networks\u003cbr\u003e 11.5 Some Notes on Neural Network Implementation\u003cbr\u003e 11.6 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 12.\u003cbr\u003e Training Neural Networks with PyTorch\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 12.1 PyTorch and Training Performance\u003cbr\u003e __12.1.1 Performance Issues\u003cbr\u003e __12.1.2 What is PyTorch?\u003cbr\u003e __12.1.3 How to learn PyTorch\u003cbr\u003e 12.2 Getting Started with PyTorch\u003cbr\u003e __12.2.1 Installing PyTorch\u003cbr\u003e __12.2.2 Creating Tensors in PyTorch\u003cbr\u003e __12.2.3 Manipulating Tensor Data Types and Sizes\u003cbr\u003e __12.2.4 Applying Mathematical Operations to Tensors\u003cbr\u003e __12.2.5 chunk( ), stack( ), cat( ) functions\u003cbr\u003e 12.3 Building a PyTorch Input Pipeline \u003cbr\u003e__12.3.1 Creating a PyTorch DataLoader from a Tensor\u003cbr\u003e __12.3.2 Concatenating two tensors into one dataset\u003cbr\u003e __12.3.3 Shuffle, batch, and repeat\u003cbr\u003e __12.3.4 Creating a dataset from a file on the local disk\u003cbr\u003e __12.3.5 Loading datasets from the torchvision.datasets library\u003cbr\u003e 12.4 Building a Neural Network Model with PyTorch\u003cbr\u003e __12.4.1 PyTorch Neural Network Module (torch.nn)\u003cbr\u003e __12.4.2 Creating a Linear Regression Model\u003cbr\u003e __12.4.3 Training a Model with the torch.nn and torch.optim Modules\u003cbr\u003e __12.4.4 Building a multilayer perceptron to classify the iris dataset\u003cbr\u003e __12.4.5 Evaluating the Model on the Test Dataset\u003cbr\u003e __12.4.6 Saving and Loading Trained Models\u003cbr\u003e 12.5 Choosing an Activation Function for a Multilayer Neural Network\u003cbr\u003e __12.5.1 Summary of Logistic Functions\u003cbr\u003e __12.5.2 Multi-class probability prediction using the softmax function\u003cbr\u003e __12.5.3 Widening the Output Range with Hyperbolic Tangents\u003cbr\u003e __12.5.4 Relu Activation Function\u003cbr\u003e 12.6 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 13.\u003cbr\u003e Learn more about PyTorch architecture\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 13.1 Key Features of PyTorch \u003cbr\u003e13.2 PyTorch Computational Graphs\u003cbr\u003e __13.2.1 Understanding Computational Graphs\u003cbr\u003e __13.2.2 Creating graphs with PyTorch\u003cbr\u003e 13.3 PyTorch Tensor Objects for Saving and Updating Model Parameters\u003cbr\u003e 13.4 Computing gradients using automatic differentiation\u003cbr\u003e __13.4.1 Computing the gradient of the loss with respect to the trainable variables\u003cbr\u003e __13.4.2 Understanding Automatic Differentiation\u003cbr\u003e __13.4.3 Hostile Sample\u003cbr\u003e 13.5 Implementing Common Architectures Using the torch.nn Module\u003cbr\u003e __13.5.1 Implementing a model based on nn.Sequential\u003cbr\u003e __13.5.2 Choosing a Loss Function\u003cbr\u003e __13.5.3 Solving the XOR classification problem\u003cbr\u003e __13.5.4 Building Flexible Models with nn.Modules\u003cbr\u003e __13.5.5 Creating Custom Layers in PyTorch\u003cbr\u003e 13.6 Project 1: Predicting Automobile Fuel Economy\u003cbr\u003e __13.6.1 Using Attribute Columns\u003cbr\u003e __13.6.2 Training a DNN Regression Model\u003cbr\u003e 13.7 Project 2: Classifying Handwritten Digits from MNIST\u003cbr\u003e 13.8 High-Level PyTorch API: Introducing PyTorch Lightning\u003cbr\u003e __13.8.1 Preparing the PyTorch Lightning Model \u003cbr\u003e__13.8.2 Preparing the Data Loader for Lightning\u003cbr\u003e __13.8.3 Training a Model Using the Lightning Trainer Class\u003cbr\u003e __13.8.4 Evaluating Models with TensorBoard\u003cbr\u003e 13.9 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 14.\u003cbr\u003e Image classification with deep convolutional neural networks\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 14.1 Components of a Convolutional Neural Network\u003cbr\u003e __14.1.1 CNN and Feature Layer Learning\u003cbr\u003e __14.1.2 Performing discrete convolution\u003cbr\u003e __14.1.3 Subsampling\u003cbr\u003e 14.2 Building a Deep Convolutional Neural Network Using Basic Building Blocks\u003cbr\u003e __14.2.1 Handling Multiple Inputs or Color Channels\u003cbr\u003e __14.2.2 Regularizing Neural Networks with L2 Regularization and Dropout\u003cbr\u003e __14.2.3 Loss function for classification\u003cbr\u003e 14.3 Implementing a Deep Convolutional Neural Network Using PyTorch\u003cbr\u003e __14.3.1 Multilayer CNN structure\u003cbr\u003e __14.3.2 Data loading and preprocessing\u003cbr\u003e __14.3.3 Implementing CNN using the torch.nn module\u003cbr\u003e 14.4 Classifying Smiling Faces Using Convolutional Neural Networks\u003cbr\u003e __14.4.1 Loading the CelebA dataset\u003cbr\u003e __14.4.2 Image Conversion and Data Augmentation\u003cbr\u003e __14.4.3 Training a CNN Smiley Face Classifier\u003cbr\u003e 14.5 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 15.\u003c\/b\u003e \u003cbr\u003eModeling Sequential Data with Recurrent Neural Networks\u003cbr\u003e\u003cbr\u003e 15.1 Introduction to Sequential Data\u003cbr\u003e __15.1.1 Sequential Data Modeling: Considering Order\u003cbr\u003e __15.1.2 Sequential data vs. time series data\u003cbr\u003e __15.1.3 Sequence Representation\u003cbr\u003e __15.1.4 Types of Sequence Modeling\u003cbr\u003e 15.2 RNNs for Sequence Modeling\u003cbr\u003e __15.2.1 Understanding the RNN Repetitive Structure\u003cbr\u003e __15.2.2 Calculating the activation output of the RNN\u003cbr\u003e __15.2.3 Hidden and Output Loops\u003cbr\u003e __15.2.4 The Difficulty of Learning Long Sequences\u003cbr\u003e __15.2.5 LSTM cell\u003cbr\u003e 15.3 Implementing RNNs for Sequence Modeling with PyTorch\u003cbr\u003e __15.3.1 First Project: Sentiment Analysis of IMDb Movie Reviews\u003cbr\u003e __15.3.2 Second Project: Implementing a Character-Level Language Model with TensorFlow\u003cbr\u003e 15.4 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 16.\u003cbr\u003e Transformer: Improving Natural Language Processing Performance with Attention Mechanisms\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 16.1 RNN with added attention mechanism\u003cbr\u003e __16.1.1 Attention to Help RNNs Retrieve Information\u003cbr\u003e __16.1.2 Original Attention Mechanism for RNNs\u003cbr\u003e __16.1.3 Processing input with a bidirectional RNN\u003cbr\u003e __16.1.4 Generating Output from a Context Vector \u003cbr\u003e__16.1.5 Calculating Attention Weights\u003cbr\u003e 16.2 Introducing the Self-Attention Mechanism\u003cbr\u003e __16.2.1 Basic forms of self-attention\u003cbr\u003e __16.2.2 Trainable Self-Attention Mechanism: Scaled Dot Product Attention\u003cbr\u003e 16.3 All You Need Is Attention: The Original Transformer Architecture\u003cbr\u003e __16.3.1 Encoding contextual embeddings with multi-head attention\u003cbr\u003e __16.3.2 Language Model Training: Decoder and Masked Multi-Head Attention\u003cbr\u003e __16.3.3 Implementation Details: Positional Encoding and Layer Normalization\u003cbr\u003e 16.4 Building Large-Scale Language Models Using Unlabeled Data\u003cbr\u003e __16.4.1 Pretraining and Fine-Tuning the Transformer Model\u003cbr\u003e __16.4.2 Using Unlabeled Data with GPT\u003cbr\u003e __16.4.3 Generating new text using GPT-2\u003cbr\u003e __16.4.4 Bidirectional Pretraining with BERT\u003cbr\u003e __16.4.5 BART combines the strengths of both\u003cbr\u003e 16.5 Fine-Tuning a BERT Model in PyTorch\u003cbr\u003e __16.5.1 Loading the IMDb Movie Review Dataset\u003cbr\u003e __16.5.2 Dataset Tokenization 715 \u003cbr\u003e__16.5.3 Loading and Fine-Tuning a Pretrained BERT Model\u003cbr\u003e __16.5.4 Easily fine-tune transformers using the trainer API\u003cbr\u003e 16.6 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 17.\u003cbr\u003e Generative Adversarial Networks for New Data Synthesis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 17.1 Introduction to Generative Adversarial Networks\u003cbr\u003e __17.1.1 Autoencoder\u003cbr\u003e __17.1.2 Generative Model for New Data Synthesis\u003cbr\u003e __17.1.3 Generating new samples with GAN\u003cbr\u003e __17.1.4 Understanding the generator and discriminator loss functions of GANs\u003cbr\u003e 17.2 Implementing a GAN Model from Scratch\u003cbr\u003e __17.2.1 Training a GAN Model in Google Colab\u003cbr\u003e __17.2.2 Implementing generator and discriminator neural networks\u003cbr\u003e __17.2.3 Defining the training dataset\u003cbr\u003e __17.2.4 Training a GAN Model\u003cbr\u003e 17.3 Improving Synthetic Image Quality with Convolutional GANs and Wasserstein GANs\u003cbr\u003e __17.3.1 Transpose convolution\u003cbr\u003e __17.3.2 Batch Normalization\u003cbr\u003e __17.3.3 Implementing constructors and discriminators\u003cbr\u003e __17.3.4 Measuring the distance between two distributions\u003cbr\u003e __17.3.5 Using EM Distance in GANs\u003cbr\u003e __17.3.6 Gradient Penalty\u003cbr\u003e __17.3.7 Training a DCGAN Model with WGAN-GP\u003cbr\u003e __17.3.8 Mode Collapse \u003cbr\u003e17.4 Other GAN Applications\u003cbr\u003e 17.5 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 18.\u003cbr\u003e Graph neural networks for dependency detection in graph-structured data\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 18.1 Introduction to Graph Data\u003cbr\u003e __18.1.1 Undirected graph\u003cbr\u003e __18.1.2 Directed Graph\u003cbr\u003e __18.1.3 Label Graph\u003cbr\u003e __18.1.4 Representing Molecules Graphically\u003cbr\u003e 18.2 Understanding Graph Convolution\u003cbr\u003e __18.2.1 Motivation for using graph convolution\u003cbr\u003e __18.2.2 Basic Graph Convolution Implementation\u003cbr\u003e 18.3 Implementing GNN from Scratch in PyTorch\u003cbr\u003e __18.3.1 Defining the NodeNetwork Model\u003cbr\u003e __18.3.2 Creating a Graph Convolution Layer in NodeNetwork\u003cbr\u003e __18.3.3 Adding a global pooling layer to handle different graph sizes\u003cbr\u003e __18.3.4 Preparing the Data Loader\u003cbr\u003e __18.3.5 Predicting using a node network\u003cbr\u003e 18.4 Implementing GNNs Using the PyTorch Geometric Library\u003cbr\u003e 18.5 Other GNN Layers and Recent Developments\u003cbr\u003e __18.5.1 Spectral Graph Convolution\u003cbr\u003e __18.5.2 Pooling\u003cbr\u003e __18.5.3 Normalization\u003cbr\u003e __18.5.4 Other advanced graph neural networks\u003cbr\u003e 18.6 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 19.\u003c\/b\u003e \u003cbr\u003eDecision-making in complex environments with reinforcement learning\u003cbr\u003e\u003cbr\u003e 19.1 Learning from experience\u003cbr\u003e __19.1.1 Understanding Reinforcement Learning\u003cbr\u003e __19.1.2 Defining the Agent-Environment Interface for Reinforcement Learning Systems\u003cbr\u003e 19.2 Basic Theory of Reinforcement Learning\u003cbr\u003e __19.2.1 Markov Decision Process\u003cbr\u003e __19.2.2 Mathematical formula for Markov decision process\u003cbr\u003e __19.2.3 Reinforcement Learning Terminology: Payoff, Policy, and Value Function\u003cbr\u003e __19.2.4 Dynamic programming using Bellman equation\u003cbr\u003e 19.3 Reinforcement Learning Algorithms\u003cbr\u003e __19.3.1 Dynamic Programming\u003cbr\u003e __19.3.2 Reinforcement Learning Using Monte Carlo\u003cbr\u003e __19.3.3 Time difference learning\u003cbr\u003e 19.4 Implementing Your First Reinforcement Learning Algorithm\u003cbr\u003e __19.4.1 Introducing the OpenAI Gym Toolkit\u003cbr\u003e __19.4.2 Solving Grid World Problems with Q-Learning\u003cbr\u003e 19.5 Deep Q-Learning\u003cbr\u003e __19.5.1 Training the DQN model using the Q-learning algorithm\u003cbr\u003e __19.5.2 Implementing a Deep Q-Learning Algorithm\u003cbr\u003e 19.6 Overall Summary\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate4357\/MidCate004\/435639658.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eInto the book\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e  \u003cdiv\u003eWhether you're aspiring to become a machine learning engineer with exceptional problem-solving skills or considering gaining experience in machine learning research, this book will help.\u003cbr\u003e Beginners may be overwhelmed by the theoretical background of machine learning.\u003cbr\u003e Recently published tutorials will teach you how to implement high-performance learning algorithms.\u003cbr\u003e Working with practical code examples and machine learning application examples is a great way to get started.\u003cbr\u003e Putting what you've learned into practice with concrete examples helps you understand broader concepts.\u003cbr\u003e But remember, with all that good comes responsibility! This book will help you practice machine learning using the Python programming language and Python-based machine learning libraries.\u003cbr\u003e In addition, we introduce the mathematical theory of machine learning algorithms. \u003cbr\u003eThis is an essential part of successfully using machine learning.\u003cbr\u003e Therefore, unlike other practical books, the book explains essential machine learning theory.\u003cbr\u003e It also provides an easy-to-understand explanation of how machine learning algorithms work, how to use them, and how to avoid common mistakes.\u003cbr\u003e This book covers the core topics and concepts you need to get started in this field.\u003cbr\u003e If you're interested in learning more, you can follow the important innovations in this field by referencing the materials presented in this book.\u003cbr\u003e\n\n\u003c\/div\u003e\n\u003cdiv\u003e --- From the Author's Note\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eWith clear explanations, mathematics and practical examples\u003cbr\u003e I completely understand the fundamental principles!\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e - Balanced explanation of theory and code!\u003cbr\u003e Running code alone isn't enough to fully understand machine learning and deep learning. \u003cbr\u003eIf you want to truly understand machine learning and deep learning, you need to understand the mathematical concepts behind the related theories and algorithms in addition to the code.\u003cbr\u003e This book provides a balanced approach between theory and code, offering clear explanations, explaining how core machine learning and deep learning algorithms work and how to use them, the underlying mathematics, practical examples, and how to avoid common pitfalls.\u003cbr\u003e\u003cbr\u003e - From core algorithms to the latest technologies!\u003cbr\u003e Machine learning is explained using Python-based core libraries (SciPy, NumPy, Scikit-Learn, Pandas, etc.) and deep learning is explained using PyTorch.\u003cbr\u003e Because PyTorch is taught in a Python-like manner, it is easier to learn and code more simply.\u003cbr\u003e The book covers the core concepts of PyTorch, as well as GANs and reinforcement learning in detail. \u003cbr\u003eIn addition to the content covered in the existing 『Machine Learning Textbook Revised 3rd Edition』, we have added the latest trends such as Transformer, PyTorch Lightning, XGBoost, and graph neural networks.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e - Learn with practical examples!\u003cbr\u003e All examples in the book are based on the author's extensive teaching and field experience as a machine learning\/deep learning expert.\u003cbr\u003e It is composed of practical and expandable examples rather than simply learning the concepts.\u003cbr\u003e By studying these examples, you will gain a solid understanding of machine learning and deep learning concepts, core algorithms, and application tips. After learning, you will also learn the principles that will allow you to build your own models and applications.\u003cbr\u003e\u003cbr\u003e Discord community for readers: https:\/\/bit.ly\/tensor-discord\u003cbr\u003e Q\u0026amp;A Open Chat Room: https:\/\/bit.ly\/tensor-chat \u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eDate of issue:\u003c\/strong\u003e November 30, 2023\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 876 pages | 1,693g | 183*235*36mm\u003c\/div\u003e\n\n \u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e- \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9791140707362 \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default Title","offer_id":43893369274410,"sku":"139613","price":74.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/d3c55a68a8bbd1dfd7fb3444bbdd337e.jpg?v=1765398682","url":"https:\/\/librairie.coreenne.fr\/en\/products\/139613","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}