{"product_id":"110446","title":"Hands-on Machine Learning ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jYEEONJTrxJLyOjJETUDM1xs1WemY.png?v=1765102582\" 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 Hands-on Machine Learning \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\/122338517\/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\u003eFrom practical examples to the latest machine learning trends like stable diffusion.\u003c\/b\u003e \u003cbr\u003eThe best practical guides presented by experts at major AI conferences.\u003cbr\u003e\u003cbr\u003e ** Decomposition for the convenience of readers (Volumes 1 and 2)\u003cbr\u003e **Updated full code to latest library version\u003cbr\u003e ** Includes \"Practice Problems + Answers\" and \"Machine Learning Project Checklist\"\u003cbr\u003e If mathematics has \"The Essentials of Mathematics,\" artificial intelligence has \"Hands-On Machine Learning\"!\u003cbr\u003e\u003cbr\u003e \"Hands-On Machine Learning\" has been further upgraded and reflects feedback from the first and second editions, and is now available in its third edition.\u003cbr\u003e To more effectively achieve the goal of \"learning while actually implementing machine learning,\" we've structured complex topics and improved them to allow for sequential learning based on difficulty.\u003cbr\u003e We've also refined and supplemented the existing explanations to make them more user-friendly and clearer so that anyone can easily understand them. \u003cbr\u003eFinally, as this is a rapidly evolving field, we've updated the entire code version and technology trends to the latest information (you can check out the updates to the third edition in the \"Publisher's Review\" below).\u003cbr\u003e Even beginners with no prior knowledge of machine learning can easily practice using Jupyter Notebooks available online.\u003cbr\u003e Translator Park Hae-seon's helpful additional explanations are also included, allowing you to learn easily and without frustration.\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\u003e[Part 1: Machine Learning]\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 1: Machine Learning at a Glance\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 1.1 What is machine learning?\u003cbr\u003e 1.2 Why use machine learning?\u003cbr\u003e 1.3 Application Cases\u003cbr\u003e 1.4 Types of Machine Learning Systems\u003cbr\u003e _1.4.1 Training Guidance Method\u003cbr\u003e __Supervised learning\u003cbr\u003e __Unsupervised learning\u003cbr\u003e __Readiness Learning\u003cbr\u003e __Self-directed learning\u003cbr\u003e __Reinforcement learning\u003cbr\u003e _1.4.2 Batch Learning and Online Learning\u003cbr\u003e __Batch learning\u003cbr\u003e __Online learning \u003cbr\u003e_1.4.3 Case-Based Learning and Model-Based Learning\u003cbr\u003e __Case-Based Learning\u003cbr\u003e __Model-based learning\u003cbr\u003e 1.5 Key Challenges in Machine Learning\u003cbr\u003e _1.5.1 Insufficient amount of training data\u003cbr\u003e _1.5.2 Non-representative training data\u003cbr\u003e _1.5.3 Low quality data\u003cbr\u003e _1.5.4 Unrelated characteristics\u003cbr\u003e _1.5.5 Overfitting training data\u003cbr\u003e _1.5.6 Underfitting the training data\u003cbr\u003e _1.5.7 Key Summary\u003cbr\u003e 1.6 Testing and Validation\u003cbr\u003e _1.6.1 Hyperparameter Tuning and Model Selection\u003cbr\u003e _1.6.2 Data inconsistency\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 2 Machine Learning Projects from Start to Finish\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 2.1 Working with Real Data\u003cbr\u003e 2.2 Seeing the Big Picture\u003cbr\u003e _2.2.1 Problem Definition\u003cbr\u003e _2.2.2 Selecting Performance Measurement Indicators\u003cbr\u003e _2.2.3 Home Inspection\u003cbr\u003e 2.3 Importing Data\u003cbr\u003e _2.3.1 Running example code using Google Colab\u003cbr\u003e _2.3.2 Saving code and data\u003cbr\u003e _2.3.3 The Convenience and Risks of Interactive Environments\u003cbr\u003e _2.3.4 Code in the book and code in the notebook\u003cbr\u003e _2.3.5 Data Download\u003cbr\u003e _2.3.6 Data Structure Overview\u003cbr\u003e _2.3.7 Creating a test set \u003cbr\u003e2.4 Exploration and Visualization for Data Understanding\u003cbr\u003e _2.4.1 Visualizing Geographic Data\u003cbr\u003e _2.4.2 Investigating Correlations\u003cbr\u003e _2.4.3 Experimenting with trait combinations\u003cbr\u003e 2.5 Preparing Data for Machine Learning Algorithms\u003cbr\u003e _2.5.1 Data Cleaning\u003cbr\u003e _2.5.2 Handling Text and Categorical Features\u003cbr\u003e _2.5.3 Feature Scales and Transforms\u003cbr\u003e _2.5.4 Custom Converter\u003cbr\u003e _2.5.5 Conversion Pipeline\u003cbr\u003e 2.6 Model Selection and Training\u003cbr\u003e _2.6.1 Training and evaluating on the training set\u003cbr\u003e _2.6.2 Evaluating with cross-validation\u003cbr\u003e 2.7 Model Fine Tuning\u003cbr\u003e _2.7.1 Grid Search\u003cbr\u003e _2.7.2 Random Search\u003cbr\u003e _2.7.3 Ensemble Methods\u003cbr\u003e _2.7.4 Best Model and Error Analysis\u003cbr\u003e _2.7.5 Evaluating the System with a Test Set\u003cbr\u003e 2.8 Launching, Monitoring, and System Maintenance\u003cbr\u003e 2.9 Try it yourself!\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 3 Classification\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 3.1 MNIST\u003cbr\u003e 3.2 Binary classifier training\u003cbr\u003e 3.3 Performance Measurement\u003cbr\u003e _3.3.1 Measuring accuracy using cross-validation\u003cbr\u003e _3.3.2 Error matrix\u003cbr\u003e _3.3.3 Precision and Recall\u003cbr\u003e _3.3.4 Precision\/Recall Tradeoff\u003cbr\u003e _3.3.5 ROC curve\u003cbr\u003e 3.4 Multi-classification \u003cbr\u003e3.5 Error Analysis\u003cbr\u003e 3.6 Multi-label classification\u003cbr\u003e 3.7 Multi-output classification\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 4 Model Training\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 4.1 Linear regression\u003cbr\u003e _4.1.1 Normal equations\u003cbr\u003e _4.1.2 Computational Complexity\u003cbr\u003e 4.2 Gradient descent\u003cbr\u003e _4.2.1 Batch gradient descent\u003cbr\u003e _4.2.2 Stochastic Gradient Descent\u003cbr\u003e _4.2.3 Mini-batch gradient descent\u003cbr\u003e 4.3 Polynomial regression\u003cbr\u003e 4.4 Learning Curve\u003cbr\u003e 4.5 Linear model with regulation\u003cbr\u003e _4.5.1 Ridge Regression\u003cbr\u003e _4.5.2 Lasso Regression\u003cbr\u003e _4.5.3 ElasticNet\u003cbr\u003e _4.5.4 Early Termination\u003cbr\u003e 4.6 Logistic Regression\u003cbr\u003e _4.6.1 Probability Estimation\u003cbr\u003e _4.6.2 Training and Cost Functions\u003cbr\u003e _4.6.3 Decision Boundary\u003cbr\u003e _4.6.4 Softmax Regression\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 5 Support Vector Machines\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 5.1 Linear SVM Classification\u003cbr\u003e _5.1.1 Soft Margin Classification\u003cbr\u003e 5.2 Nonlinear SVM classification\u003cbr\u003e _5.2.1 Polynomial Kernel\u003cbr\u003e _5.2.2 Similarity characteristics\u003cbr\u003e _5.2.3 Gaussian RBF kernel\u003cbr\u003e _5.2.4 Computational Complexity\u003cbr\u003e 5.3 SVM regression\u003cbr\u003e 5.4 SVM theory\u003cbr\u003e 5.5 Dual Problem\u003cbr\u003e _5.5.1 Kernel SVM\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 6 Decision Trees\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 6.1 Decision Tree Learning and Visualization\u003cbr\u003e 6.2 Prediction\u003cbr\u003e 6.3 Class probability estimation\u003cbr\u003e 6.4 CART training algorithm\u003cbr\u003e 6.5 Computational Complexity \u003cbr\u003e6.6 Gini impurity or entropy?\u003cbr\u003e 6.7 Regulatory Parameters\u003cbr\u003e 6.8 Regression\u003cbr\u003e 6.9 Sensitivity to axial direction\u003cbr\u003e 6.10 Distribution Problems in Decision Trees\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 7: Ensemble Learning and Random Forests\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 7.1 Voting-based classifier\u003cbr\u003e 7.2 Bagging and Pasting\u003cbr\u003e _7.2.1 Bagging and Pasting in Scikit-learn\u003cbr\u003e _7.2.2 OOB Evaluation\u003cbr\u003e 7.3 Random Patches and Random Subspaces\u003cbr\u003e 7.4 Random Forest\u003cbr\u003e _7.4.1 Extra Tree\u003cbr\u003e _7.4.2 Feature Importance\u003cbr\u003e 7.5 Boosting\u003cbr\u003e 7.5.1 AdaBoost\u003cbr\u003e _7.5.2 Gradient Boosting\u003cbr\u003e _7.5.3 Histogram-based gradient boosting\u003cbr\u003e _7.6 Stacking\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 8 Dimensional Reduction\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 8.1 The Curse of Dimensions\u003cbr\u003e 8.2 Approaches to Dimensionality Reduction\u003cbr\u003e _8.2.1 Projection\u003cbr\u003e _8.2.2 Manifold Learning\u003cbr\u003e 8.3 Principal component analysis\u003cbr\u003e _8.3.1 Distributed Preservation\u003cbr\u003e _8.3.2 Main ingredients\u003cbr\u003e _8.3.3 Projecting to d-dimension\u003cbr\u003e _8.3.4 Using scikit-learn\u003cbr\u003e _8.3.5 Proportion of Variance Explained\u003cbr\u003e _8.3.6 Choosing the Appropriate Number of Dimensions\u003cbr\u003e _8.3.7 PCA for Compression\u003cbr\u003e _8.3.8 Random PCA\u003cbr\u003e _8.3.9 Progressive PCA\u003cbr\u003e 8.4 Random projection\u003cbr\u003e 8.5 Local linear embedding \u003cbr\u003e8.6 Other dimensionality reduction techniques\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 9 Unsupervised Learning\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 9.1 Cluster\u003cbr\u003e _9.1.1 k-means\u003cbr\u003e __k-means algorithm\u003cbr\u003e __How to initialize Centroid\u003cbr\u003e __k-means speedup and mini-batch k-means\u003cbr\u003e __Finding the optimal number of clusters\u003cbr\u003e _9.1.2 Limitations of k-means\u003cbr\u003e _9.1.3 Image segmentation using clusters\u003cbr\u003e _9.1.4 Semi-supervised learning using clusters\u003cbr\u003e _9.1.5 DBSCAN\u003cbr\u003e _9.1.6 Other clustering algorithms\u003cbr\u003e 9.2 Gaussian Mixture\u003cbr\u003e _9.2.1 Outlier Detection Using Gaussian Mixtures\u003cbr\u003e _9.2.2 Selecting the number of clusters\u003cbr\u003e _9.2.3 Bayesian Gaussian Mixture Model\u003cbr\u003e _9.2.4 Algorithms for Outlier Detection and Outlier Detection\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Part 2: Neural Networks and Deep Learning]\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 10: Introduction to Artificial Neural Networks Using Keras\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 10.1 From Biological Neurons to Artificial Neurons\u003cbr\u003e _10.1.1 Biological Neurons\u003cbr\u003e _10.1.2 Logical Operations Using Neurons\u003cbr\u003e _10.1.3 Perceptron\u003cbr\u003e _10.1.4 Multilayer Perceptron and Backpropagation\u003cbr\u003e _10.1.5 Multilayer Perceptron for Regression\u003cbr\u003e _10.1.6 Multilayer Perceptron for Classification \u003cbr\u003e10.2 Implementing a Multilayer Perceptron with Keras\u003cbr\u003e _10.2.1 Building an Image Classifier with the Sequential API\u003cbr\u003e __Loading a Dataset with Keras\u003cbr\u003e __Creating a model with the Sequential API\u003cbr\u003e __Compile model\u003cbr\u003e __Model training and evaluation\u003cbr\u003e Making predictions with the __model\u003cbr\u003e _10.2.2 Building a Multilayer Perceptron for Regression with the Sequential API\u003cbr\u003e _10.2.3 Building Complex Models with the Functional API\u003cbr\u003e _10.2.4 Creating Dynamic Models with the Subclassing API\u003cbr\u003e _10.2.5 Saving and Restoring Models\u003cbr\u003e _10.2.6 Using Callbacks\u003cbr\u003e _10.2.7 Visualizing with TensorBoard\u003cbr\u003e 10.3 Tuning Neural Network Hyperparameters\u003cbr\u003e _10.3.1 Number of hidden layers\u003cbr\u003e _10.3.2 Number of neurons in the hidden layer\u003cbr\u003e _10.3.3 Learning rate, batch size, and other hyperparameters\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 11: Training Deep Neural Networks\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 11.1 Gradient Vanishing and Runaway Problems\u003cbr\u003e _11.1.1 Glorot and He Initialization\u003cbr\u003e _11.1.2 Advanced Activation Functions\u003cbr\u003e __LeakyReLU\u003cbr\u003e __ELU and SELU\u003cbr\u003e __GELU, Swish, Mish\u003cbr\u003e _11.1.3 Batch Normalization\u003cbr\u003e __Implementing Batch Normalization with Keras\u003cbr\u003e _11.1.4 Gradient Clipping \u003cbr\u003e11.2 Reusing pretrained layers\u003cbr\u003e _11.2.1 Transfer Learning with Keras\u003cbr\u003e _11.2.2 Unsupervised Pretraining\u003cbr\u003e _11.2.3 Pre-training in auxiliary tasks\u003cbr\u003e 11.3 High-Speed ​​Optimizer\u003cbr\u003e _11.3.1 Momentum Optimization\u003cbr\u003e _11.3.2 Nesterov Acceleration Slope\u003cbr\u003e _11.3.3 AdaGrad\u003cbr\u003e _11.3.4 RMSProp\u003cbr\u003e _11.3.5 Adam\u003cbr\u003e _11.3.6 AdaMax\u003cbr\u003e _11.3.7 Nadam\u003cbr\u003e _11.3.8 AdamW\u003cbr\u003e _11.3.9 Learning Rate Scheduling\u003cbr\u003e 11.4 Avoiding Overfitting Using Regularization\u003cbr\u003e _11.4.1 l1 and l2 regulation\u003cbr\u003e _11.4.2 Dropout\u003cbr\u003e _11.4.3 Monte Carlo Dropout\u003cbr\u003e _11.4.4 Max-norm regulation\u003cbr\u003e 11.5 Summary and Practical Guidelines\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 12: Custom Models and Training with TensorFlow\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 12.1 Overview of TensorFlow\u003cbr\u003e 12.2 Using TensorFlow Like NumPy\u003cbr\u003e _12.2.1 Tensors and Operations\u003cbr\u003e _12.2.2 Tensors and NumPy\u003cbr\u003e _12.2.3 Type Conversion\u003cbr\u003e _12.2.4 Variables\u003cbr\u003e _12.2.5 Other data structures\u003cbr\u003e 12.3 Custom Models and Training Algorithms\u003cbr\u003e _12.3.1 User-defined loss functions\u003cbr\u003e _12.3.2 Saving and Loading Models with Custom Elements \u003cbr\u003e_12.3.3 Customizing the activation function, initialization, regulation, and limits\u003cbr\u003e _12.3.4 Custom Metrics\u003cbr\u003e _12.3.5 Custom Layers\u003cbr\u003e _12.3.6 Custom Models\u003cbr\u003e _12.3.7 Losses and metrics based on model components\u003cbr\u003e _12.3.8 Computing Gradients with Automatic Differentiation\u003cbr\u003e _12.3.9 Custom Training Iterations\u003cbr\u003e 12.4 TensorFlow Functions and Graphs\u003cbr\u003e _12.4.1 Autograph and Tracing\u003cbr\u003e _12.4.2 How to use TensorFlow functions\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 13: Data Loading and Preprocessing with TensorFlow\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 13.1 Data API\u003cbr\u003e _13.1.1 Chain transformation\u003cbr\u003e _13.1.2 Data Shuffling\u003cbr\u003e _13.1.3 Reading lines from multiple files one at a time\u003cbr\u003e _13.1.4 Data Preprocessing\u003cbr\u003e _13.1.5 Combining Data Loading and Preprocessing\u003cbr\u003e _13.1.6 Prefetch\u003cbr\u003e _13.1.7 Using Keras and Datasets\u003cbr\u003e 13.2 TFRecord Format\u003cbr\u003e _13.2.1 Compressed TFRecord file\u003cbr\u003e _13.2.2 Protocol Buffers Overview\u003cbr\u003e _13.2.3 TensorFlow Protocol Buffers\u003cbr\u003e _13.2.4 Example Reading and Parsing Protocol Buffer  \u003cbr\u003e_13.2.5 Handling Lists of Lists with SequenceExample Protocol Buffers\u003cbr\u003e 13.3 Preprocessing Layers in Keras\u003cbr\u003e _13.3.1 Normalization layer\u003cbr\u003e _13.3.2 Discretization layer\u003cbr\u003e _13.3.3 CategoryEncoding layer\u003cbr\u003e _13.3.4 StringLookup layer\u003cbr\u003e _13.3.5 Hashing Layer\u003cbr\u003e _13.3.6 Encoding categorical features using embeddings\u003cbr\u003e _13.3.7 Text Preprocessing\u003cbr\u003e _13.3.8 Using Pretrained Language Model Components\u003cbr\u003e _13.3.9 Image Preprocessing Layer\u003cbr\u003e 13.5 TensorFlow Dataset Project\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 14: Computer Vision Using Convolutional Neural Networks\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 14.1 Visual Cortex Structure\u003cbr\u003e 14.2 Convolutional Layer\u003cbr\u003e _14.2.1 Filter\u003cbr\u003e _14.2.2 Stacking Multiple Feature Maps\u003cbr\u003e _14.2.3 Implementing a Convolutional Layer with Keras\u003cbr\u003e _14.2.4 Memory Requirements\u003cbr\u003e 14.3 Pooling layer\u003cbr\u003e 14.4 Implementing a Pooling Layer in Keras\u003cbr\u003e 14.5 CNN Structure\u003cbr\u003e _14.5.1 LeNet-5\u003cbr\u003e _14.5.2 AlexNet\u003cbr\u003e _14.5.3 GoogLeNet\u003cbr\u003e _14.5.4 VGGNet\u003cbr\u003e _14.5.5 ResNet\u003cbr\u003e _14.5.6 Xception\u003cbr\u003e _14.5.7 SENet\u003cbr\u003e _14.5.8 Other notable structures\u003cbr\u003e _14.5.9 Choosing the Right CNN Architecture\u003cbr\u003e 14.6 Implementing a ResNet-34 CNN with Keras \u003cbr\u003e14.7 Using Pretrained Models in Keras\u003cbr\u003e 14.8 Transfer Learning Using Pretrained Models\u003cbr\u003e 14.9 Classification and Location Estimation\u003cbr\u003e 14.10 Object Detection\u003cbr\u003e _14.10.1 Fully Convolutional Neural Networks\u003cbr\u003e _14.10.2 YOLO\u003cbr\u003e 14.11 Object Tracking\u003cbr\u003e 14.12 Semantic Segmentation\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 15 Sequence Processing Using RNNs and CNNs\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 15.1 Circular Neurons and Circular Layers\u003cbr\u003e _15.1.1 Memory Cells\u003cbr\u003e _15.1.2 Input and Output Sequences\u003cbr\u003e 15.2 Training the RNN\u003cbr\u003e 15.3 Forecasting Time Series\u003cbr\u003e _15.3.1 ARMA Model\u003cbr\u003e _15.3.2 Preparing Data for Machine Learning Models\u003cbr\u003e _15.3.3 Predicting with a linear model\u003cbr\u003e _15.3.4 Predicting with a Simple RNN\u003cbr\u003e _15.3.5 Predicting with Deep RNNs\u003cbr\u003e _15.3.6 Forecasting Multivariate Time Series\u003cbr\u003e _15.3.7 Predicting Multiple Time Steps Ahead\u003cbr\u003e _15.3.8 Predicting with a Sequence-to-Sequence Model\u003cbr\u003e 15.4 Handling Long Sequences\u003cbr\u003e _15.4.1 Fighting the Unstable Gradient Problem\u003cbr\u003e _15.4.2 Solving short-term memory problems\u003cbr\u003e __LSTM cell\u003cbr\u003e __GRU cell\u003cbr\u003e Processing sequences with __1D convolutional layers\u003cbr\u003e __WaveNet\u003cbr\u003e Practice problems\u003cbr\u003e \u003cbr\u003e\u003cb\u003eChapter 16: Natural Language Processing Using RNNs and Attention\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 16.1 Generating Shakespearean Styles with Char-RNN\u003cbr\u003e _16.1.1 Creating a Training Dataset\u003cbr\u003e _16.1.2 Building and Training a Char-RNN Model\u003cbr\u003e _16.1.3 Generating fake Shakespeare text\u003cbr\u003e _16.1.4 RNN with state\u003cbr\u003e 16.2 Sentiment Analysis\u003cbr\u003e _16.2.1 Masking\u003cbr\u003e _16.2.2 Reusing Pretrained Embeddings and Language Models\u003cbr\u003e 16.3 Encoder-Decoder Networks for Neural Machine Translation\u003cbr\u003e _16.3.1 Bidirectional RNN\u003cbr\u003e _16.3.2 Beam Search\u003cbr\u003e 16.4 Attention Mechanism\u003cbr\u003e _16.4.1 Transformer Structure: All You Need Is Attention\u003cbr\u003e __Position encoding\u003cbr\u003e __Multi-head attention\u003cbr\u003e 16.5 Recent Innovations in Language Modeling\u003cbr\u003e 16.6 Vision Transformer\u003cbr\u003e 16.7 Hugging Face's Transformers Library\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 17: Autoencoders, GANs, and Diffusion Models\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 17.1 Efficient Data Representation\u003cbr\u003e 17.2 Performing PCA with an Undercomplete Linear Autoencoder\u003cbr\u003e 17.3 Stacked Autoencoders\u003cbr\u003e _17.3.1 Implementing a Stacked Autoencoder with Keras\u003cbr\u003e _17.3.2 Reconstruction Visualization \u003cbr\u003e_17.3.3 Visualizing the Fashion MNIST Dataset\u003cbr\u003e _17.3.4 Unsupervised Pretraining Using Stacked Autoencoders\u003cbr\u003e _17.3.5 Weight Binding\u003cbr\u003e _17.3.6 Training Autoencoders One by One\u003cbr\u003e 17.4 Convolutional Autoencoder\u003cbr\u003e 17.5 Noise Reduction Autoencoder\u003cbr\u003e 17.6 Sparse Autoencoder\u003cbr\u003e 17.7 Variational Autoencoder\u003cbr\u003e _17.7.1 Generating Fashion MNIST Images\u003cbr\u003e 17.8 Generative Adversarial Networks\u003cbr\u003e _17.8.1 Difficulties in GAN Training\u003cbr\u003e _17.8.2 Deep Convolutional GAN\u003cbr\u003e _17.8.3 ProGAN\u003cbr\u003e __Mini-batch standard deviation layer\u003cbr\u003e __Same learning rate\u003cbr\u003e __Pixel-wise normalization layer\u003cbr\u003e _17.8.4 StyleGAN\u003cbr\u003e __Mapping Network\u003cbr\u003e __synthetic network\u003cbr\u003e 17.9 Diffusion Model\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 18: Reinforcement Learning\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 18.1 Learning to Optimize Rewards\u003cbr\u003e 18.2 Policy Exploration\u003cbr\u003e 18.3 OpenAI Gym\u003cbr\u003e 18.4 Neural Network Policy\u003cbr\u003e 18.5 Behavioral Evaluation: The Credit Assignment Problem\u003cbr\u003e 18.6 Policy Gradient\u003cbr\u003e 18.7 Markov Decision Processes\u003cbr\u003e 18.8 Time difference learning\u003cbr\u003e 18.9 Q-Learning\u003cbr\u003e _18.9.1 Exploration Policy\u003cbr\u003e _18.9.2 Approximate Q-Learning and Deep Q-Learning\u003cbr\u003e 18.10 Implementing Deep Q-Learning\u003cbr\u003e 18.11 Variations of Deep Q-Learning \u003cbr\u003e_18.11.1 Fixed Q-Value Target\u003cbr\u003e _18.11.2 Double DQN\u003cbr\u003e _18.11.3 Priority-based experience playback\u003cbr\u003e _18.11.4 Dueling DQN\u003cbr\u003e 18.12 Other Reinforcement Learning Algorithms\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 19: Training and Deploying Large-Scale TensorFlow Models\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 19.1 Serving TensorFlow Models\u003cbr\u003e _19.1.1 Using TensorFlow Serving\u003cbr\u003e Export to __SavedModel\u003cbr\u003e __Installing and starting TensorFlow Serving\u003cbr\u003e __Querying TF Serving with REST API\u003cbr\u003e __Querying TF Serving with the gRPC API\u003cbr\u003e __Deploying a new version of the model\u003cbr\u003e _19.1.2 Creating a Prediction Service in Vertex AI\u003cbr\u003e _19.1.3 Running Batch Prediction Jobs in Vertex AI\u003cbr\u003e 19.2 Deploying Models to Mobile or Embedded Devices\u003cbr\u003e 19.3 Running the Model on a Web Page\u003cbr\u003e 19.4 Using GPUs to Speed ​​Up Computation\u003cbr\u003e _19.4.1 Buy a GPU\u003cbr\u003e _19.4.2 Managing GPU RAM\u003cbr\u003e _19.4.3 Assigning Operations and Variables to Devices\u003cbr\u003e _19.4.4 Running in Parallel on Multiple Devices\u003cbr\u003e 19.5 Training Models on Multiple Devices\u003cbr\u003e _19.5.1 Model Parallelism\u003cbr\u003e _19.5.2 Data Parallelism \u003cbr\u003eData parallelism using the mirrored strategy\u003cbr\u003e __Data parallelism using centralized parameters\u003cbr\u003e __bandwidth saturation\u003cbr\u003e _19.5.3 Large-Scale Training Using the Distributed Strategy API\u003cbr\u003e _19.5.4 Training a Model on a TensorFlow Cluster\u003cbr\u003e _19.5.5 Running Large-Scale Training Jobs in Vertex AI\u003cbr\u003e _19.5.6 Hyperparameter Tuning for Vertex AI\u003cbr\u003e Practice problems\u003cbr\u003e In conclusion\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Part 3 Appendix]\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Appendix A: Practice Problem Answers\u003cbr\u003e\u003cbr\u003e Appendix B Machine Learning Project Checklist\u003cbr\u003e B.1 Define the problem and draw the big picture\u003cbr\u003e B.2 Collect data\u003cbr\u003e B.3 Explore the data\u003cbr\u003e B.4 Prepare the data\u003cbr\u003e B.5 Choose a few possible models\u003cbr\u003e Fine-tuning the B.6 model\u003cbr\u003e Launching the B.7 solution\u003cbr\u003e Launching the B.8 system!\u003cbr\u003e\u003cbr\u003e Appendix C Automatic Differentiation\u003cbr\u003e C.1 Manual Differentiation\u003cbr\u003e C.2 Finite difference approximation\u003cbr\u003e C.3 Forward mode automatic differentiation\u003cbr\u003e C.4 Automatic differentiation in reverse mode\u003cbr\u003e\u003cbr\u003e Appendix D Special Data Structures\u003cbr\u003e D.1 String\u003cbr\u003e D.2 Ragged tensor\u003cbr\u003e D.3 Sparse tensors\u003cbr\u003e D.4 Tensor Arrays \u003cbr\u003eD.5 Set\u003cbr\u003e D.6 Queue\u003cbr\u003e\u003cbr\u003e Appendix E TensorFlow Graph\u003cbr\u003e E.1 TF functions and concrete functions\u003cbr\u003e E.2 Exploring Function Definitions and Function Graphs\u003cbr\u003e E.3 Tracing Details\u003cbr\u003e E.4 Expressing Control Flow with Autographs\u003cbr\u003e E.5 Handling Variables and Other Resources in TF Functions\u003cbr\u003e E.6 Using (or Not Using) TF Functions with Keras\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\/TopCate4290\/MidCate007\/428965595(1).jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\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\u003eThe world's #1 bestseller, satisfying beginners and experts alike.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e This book covers both theory and practice, helping you paint a big picture across both machine learning and deep learning.\u003cbr\u003e You'll learn how to easily train models and build neural networks, especially with diagrammatic explanations and up-to-date, practical code examples.\u003cbr\u003e You can also review what you've learned and apply it to your own projects by solving practice problems provided for each chapter. \u003cbr\u003eIf you have any Python programming experience, get started right away.\u003cbr\u003e Anyone can become a machine learning expert!\u003cbr\u003e\u003cbr\u003e \u003cb\u003e**Updated in 3rd Edition**\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e -Update the entire code to the latest library version\u003cbr\u003e - Detailed model selection guidelines\u003cbr\u003e -New features in scikit-learn and Keras\u003cbr\u003e · Scikit-learn: Feature name tracking, histogram-based gradient boosting, label propagation, etc.\u003cbr\u003e · Keras: preprocessing layers, data augmentation layers, etc.\u003cbr\u003e -Added libraries not included in the 2nd edition\u003cbr\u003e Keras Tuner library for hyperparameter tuning\u003cbr\u003e · Hugging Face's Transformers library for natural language processing\u003cbr\u003e - Diffusion model (stable diffusion)\u003cbr\u003e -The latest trends and implementations in computer vision and natural language processing.\u003cbr\u003e\u003cbr\u003e \u003cb\u003e**Who is this book for?**\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e -Beginners with basic knowledge but little practical experience\u003cbr\u003e -Intermediate level users who want to improve their practical skills \u003cbr\u003eDevelopers and engineers who want to utilize machine learning in their projects.\u003cbr\u003e -Data scientists and researchers working on machine learning research or data analysis.\u003cbr\u003e\u003cbr\u003e \u003cb\u003e** Advantages of this book **\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e - Explains in detail through practical examples rather than listing complex theories.\u003cbr\u003e -You can develop practical problem-solving skills through hands-on experience and complete your own portfolio.\u003cbr\u003e - Expand your knowledge of various machine learning and deep learning models, tools, and libraries.\u003cbr\u003e - Reflects the latest trends in computer vision, natural language processing, and reinforcement learning, including stable diffusion.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e \u003cb\u003eExample source\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e https:\/\/github.com\/rickiepark\/handson-ml3 \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 September 29, 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 1,044 pages | 2,005g | 183*235*60mm\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 9791169211475\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN10:\u003c\/strong\u003e 116921147X \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 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