Artificial intelligence with Python / / Teik Toe Teoh, Zheng Rong

• Autore: Teoh Teik Toe
• Pubblicazione: Singapore : , : Springer Nature Singapore Pte Ltd., , [2022]; ©2022
• Descrizione fisica: 1 online resource (334 pages)
• Disciplina: 006.3
• Soggetto topico: Artificial intelligence - Data processing; Python (Computer program language)
• Persona (resp. second.): RongZheng
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Acknowledgments -- Contents -- Part I Python -- 1 Python for Artificial Intelligence -- 1.1 Common Uses -- 1.1.1 Relative Popularity -- 1.1.2 Features -- 1.1.3 Syntax and Design -- 1.2 Scientific Programming -- 1.3 Why Python for Artificial Intelligence -- 2 Getting Started -- 2.1 Setting up Your Python Environment -- 2.2 Anaconda -- 2.2.1 Installing Anaconda -- 2.2.2 Further Installation Steps -- 2.2.3 Updating Anaconda -- 2.3 Installing Packages -- 2.4 Virtual Environment -- 2.5 Jupyter Notebooks -- 2.5.1 Starting the Jupyter Notebook -- 2.5.2 Notebook Basics -- Running Cells -- Modal Editing -- Inserting Unicode (e.g., Greek Letters) -- A Test Program -- 2.5.3 Working with the Notebook -- Tab Completion -- On-Line Help -- Other Content -- 2.5.4 Sharing Notebooks -- 3 An Introductory Example -- 3.1 Overview -- 3.2 The Task: Plotting a White Noise Process -- 3.3 Our First Program -- 3.3.1 Imports -- Why So Many Imports? -- Packages -- Subpackages -- 3.3.2 Importing Names Directly -- 3.3.3 Random Draws -- 3.4 Alternative Implementations -- 3.4.1 A Version with a for Loop -- 3.4.2 Lists -- 3.4.3 The for Loop -- 3.4.4 A Comment on Indentation -- 3.4.5 While Loops -- 3.5 Another Application -- 3.6 Exercises -- 3.6.1 Exercise 1 -- 3.6.2 Exercise 2 -- 3.6.3 Exercise 3 -- 3.6.4 Exercise 4 -- 3.6.5 Exercise 5 -- 3.7 Solutions -- 3.7.1 Exercise 1 -- 3.7.2 Exercise 2 -- 3.7.3 Exercise 3 -- 3.7.4 Exercise 4 -- 3.7.5 Exercise 5 -- 4 Basic Python -- 4.1 Hello, World! -- 4.2 Indentation -- 4.3 Variables and Types -- 4.3.1 Numbers -- 4.3.2 Strings -- 4.3.3 Lists -- 4.3.4 Dictionaries -- 4.4 Basic Operators -- 4.4.1 Arithmetic Operators -- 4.4.2 List Operators -- 4.4.3 String Operators -- 4.5 Logical Conditions -- 4.6 Loops -- 4.7 List Comprehensions -- 4.8 Exception Handling -- 4.8.1 Sets -- 5 Intermediate Python -- 5.1 Functions. ; 5.2 Classes and Objects -- 5.3 Modules and Packages -- 5.3.1 Writing Modules -- 5.4 Built-in Modules -- 5.5 Writing Packages -- 5.6 Closures -- 5.7 Decorators -- 6 Advanced Python -- 6.1 Python Magic Methods -- 6.1.1 Exercise -- 6.1.2 Solution -- 6.2 Comprehension -- 6.3 Functional Parts -- 6.4 Iterables -- 6.5 Decorators -- 6.6 More on Object Oriented Programming -- 6.6.1 Mixins -- 6.6.2 Attribute Access Hooks -- 6.6.3 Callable Objects -- 6.6.4 _new_ vs _init_ -- 6.7 Properties -- 6.8 Metaclasses -- 7 Python for Data Analysis -- 7.1 Ethics -- 7.2 Data Analysis -- 7.2.1 Numpy Arrays -- 7.2.2 Pandas -- Selections -- 7.2.3 Matplotlib -- 7.3 Sample Code -- Part II Artificial Intelligence Basics -- 8 Introduction to Artificial Intelligence -- 8.1 Data Exploration -- 8.2 Problems with Data -- 8.3 A Language and Approach to Data-Driven Story-Telling -- 8.4 Example: Telling Story with Data -- 9 Data Wrangling -- 9.1 Handling Missing Data -- 9.1.1 Missing Data -- 9.1.2 Removing Missing Data -- 9.2 Transformation -- 9.2.1 Duplicates -- 9.2.2 Mapping -- 9.3 Outliers -- 9.4 Permutation -- 9.5 Merging and Combining -- 9.6 Reshaping and Pivoting -- 9.7 Wide to Long -- 10 Regression -- 10.1 Linear Regression -- 10.2 Decision Tree Regression -- 10.3 Random Forests -- 10.4 Neural Network -- 10.5 How to Improve Our Regression Model -- 10.5.1 Boxplot -- 10.5.2 Remove Outlier -- 10.5.3 Remove NA -- 10.6 Feature Importance -- 10.7 Sample Code -- 11 Classification -- 11.1 Logistic Regression -- 11.2 Decision Tree and Random Forest -- 11.3 Neural Network -- 11.4 Logistic Regression -- 11.5 Decision Tree -- 11.6 Feature Importance -- 11.7 Remove Outlier -- 11.8 Use Top 3 Features -- 11.9 SVM -- 11.9.1 Important Hyper Parameters -- 11.10 Naive Bayes -- 11.11 Sample Code -- 12 Clustering -- 12.1 What Is Clustering? -- 12.2 K-Means -- 12.3 The Elbow Method. ; 13 Association Rules -- 13.1 What Are Association Rules -- 13.2 Apriori Algorithm -- 13.3 Measures for Association Rules -- Part III Artificial Intelligence Implementations -- 14 Text Mining -- 14.1 Read Data -- 14.2 Date Range -- 14.3 Category Distribution -- 14.4 Texts for Classification -- 14.5 Vectorize -- 14.6 CountVectorizer -- 14.7 TF-IDF -- 14.8 Feature Extraction with TF-IDF -- 14.9 Sample Code -- 15 Image Processing -- 15.1 Load the Dependencies -- 15.2 Load Image from urls -- 15.3 Image Analysis -- 15.4 Image Histogram -- 15.5 Contour -- 15.6 Grayscale Transformation -- 15.7 Histogram Equalization -- 15.8 Fourier Transformation -- 15.9 High pass Filtering in FFT -- 15.10 Pattern Recognition -- 15.11 Sample Code -- 16 Convolutional Neural Networks -- 16.1 The Convolution Operation -- 16.2 Pooling -- 16.3 Flattening -- 16.4 Exercise -- 16.5 CNN Architectures -- 16.5.1 VGG16 -- 16.5.2 Inception Net -- 16.5.3 ResNet -- 16.6 Finetuning -- 16.7 Other Tasks That Use CNNs -- 16.7.1 Object Detection -- 16.7.2 Semantic Segmentation -- 17 Chatbot, Speech, and NLP -- 17.1 Speech to Text -- 17.2 Importing the Packages for Chatbot -- 17.3 Preprocessing the Data for Chatbot -- 17.3.1 Download the Data -- 17.3.2 Reading the Data from the Files -- 17.3.3 Preparing Data for Seq2Seq Model -- 17.4 Defining the Encoder-Decoder Model -- 17.5 Training the Model -- 17.6 Defining Inference Models -- 17.7 Talking with Our Chatbot -- 17.8 Sample Code -- 18 Deep Convolutional Generative Adversarial Network -- 18.1 What Are GANs? -- 18.2 Setup -- 18.2.1 Load and Prepare the Dataset -- 18.3 Create the Models -- 18.3.1 The Generator -- 18.3.2 The Discriminator -- 18.4 Define the Loss and Optimizers -- 18.4.1 Discriminator Loss -- 18.4.2 Generator Loss -- 18.5 Save Checkpoints -- 18.6 Define the Training Loop -- 18.6.1 Train the Model -- 18.6.2 Create a GIF. ; 19 Neural Style Transfer -- 19.1 Setup -- 19.1.1 Import and Configure Modules -- 19.2 Visualize the Input -- 19.3 Fast Style Transfer Using TF-Hub -- 19.4 Define Content and Style Representations -- 19.4.1 Intermediate Layers for Style and Content -- 19.5 Build the Model -- 19.6 Calculate Style -- 19.7 Extract Style and Content -- 19.8 Run Gradient Descent -- 19.9 Total Variation Loss -- 19.10 Re-run the Optimization -- 20 Reinforcement Learning -- 20.1 Reinforcement Learning Analogy -- 20.2 Q-learning -- 20.3 Running a Trained Taxi -- Bibliography -- Index.
• Titolo autorizzato: Artificial intelligence with Python
• ISBN: 981-16-8615-7; 981-16-8614-9
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910743214603321
• Lo trovi qui: Univ. Federico II
• Serie: Machine Learning: Foundations, Methodologies, and Applications