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Artificial Intelligence in Business Management / / Teik Toe Teoh and Yu Jin Goh
| Artificial Intelligence in Business Management / / Teik Toe Teoh and Yu Jin Goh |
| Autore | Teoh Teik Toe |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (385 pages) |
| Disciplina | 658 |
| Collana | Machine Learning |
| Soggetto topico | Industrial management - Technological innovations |
| ISBN | 981-9945-58-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgments -- Contents -- Part I Artificial Intelligence Algorithms -- 1 Introduction to Artificial Intelligence -- 1.1 Introduction -- 1.2 History of Artificial Intelligence -- 1.3 Types of Artificial Intelligence Algorithms -- 1.4 Organization of the Book -- References -- 2 Regression -- 2.1 Linear Regression -- 2.2 Decision Tree Regression -- 2.3 Random Forests -- 2.4 Neural Network -- 2.5 Improving Regression Performance -- 2.5.1 Boxplot -- 2.5.2 Remove Outlier -- 2.5.3 Remove NA -- 2.5.4 Feature Importance -- Exercises -- References -- 3 Classification -- 3.1 Logistic Regression -- 3.2 Decision Tree and Random Forest -- 3.3 Neural Network -- 3.4 Support Vector Machines -- 3.4.1 Important Hyperparameters -- 3.5 Naive Bayes -- 3.6 Improving Classification Performance -- Exercises -- References -- 4 Clustering -- 4.1 Introduction to Clustering -- 4.2 K-means -- 4.3 The Elbow Method -- Exercises -- References -- 5 Time Series -- 5.1 Introduction to Time Series -- 5.2 Stationarity -- 5.3 Level, Trend, and Seasonality -- 5.4 Exponential Smoothing -- 5.4.1 Simple Exponential Smoothing -- 5.4.2 Double Exponential Smoothing (Holt's Exponential Smoothing) -- 5.4.3 Triple Exponential Smoothing (Holt-Winters Exponential Smoothing) -- 5.5 Moving Average Smoothing -- 5.6 Autoregression -- 5.7 Moving Average Process -- 5.8 SARIMA -- 5.9 ARCH/GARCH -- Exercises -- References -- 6 Convolutional Neural Networks -- 6.1 The Convolution Operation -- 6.2 Pooling -- 6.3 Flattening -- 6.4 Building a CNN -- 6.5 CNN Architectures -- 6.5.1 VGG16 -- 6.5.2 InceptionNet -- 6.5.3 ResNet -- 6.6 Finetuning -- 6.7 Other Tasks That Use CNNs -- 6.7.1 Object Detection -- 6.7.2 Semantic Segmentation -- Exercises -- References -- 7 Text Mining -- 7.1 Preparing the Data -- 7.2 Texts for Classification -- 7.3 Vectorize -- 7.4 TF-IDF -- 7.5 Web Scraping.
7.6 Tokenization -- 7.7 Part of Speech Tagging -- 7.8 Stemming and Lemmatization -- Exercises -- Reference -- 8 Chatbot, Speech, and NLP -- 8.1 Speech to Text -- 8.2 Preparing the Data for Chatbot -- 8.2.1 Download the Data -- 8.2.2 Reading the Data from the Files -- 8.2.3 Preparing Data for Seq2Seq Model -- 8.3 Defining the Encoder-Decoder Model -- 8.4 Training the Model -- 8.5 Defining Inference Models -- 8.6 Talking with Our Chatbot -- Exercises -- References -- Part II Applications of Artificial Intelligence in Business Management -- 9 AI in Human Resource Management -- 9.1 Introduction to Human Resource Management -- 9.2 Artificial Intelligence in Human Resources -- 9.3 Applications of AI in Human Resources -- 9.3.1 Salary Prediction -- 9.3.2 Recruitment -- 9.3.3 Course Recommendation -- 9.3.4 Employee Attrition Prediction -- Exercises -- References -- 10 AI in Sales -- 10.1 Introduction to Sales -- 10.1.1 The Sales Cycle -- 10.2 Artificial Intelligence in Sales -- 10.3 Applications of AI in Sales -- 10.3.1 Lead Scoring -- 10.3.2 Sales Assistant Chatbot -- 10.3.3 Product Recommender Systems -- 10.3.4 Recommending via Pairwise Correlated Purchases -- Exercises -- References -- 11 AI in Marketing -- 11.1 Introduction to Marketing -- 11.1.1 Sales vs Marketing -- 11.2 Artificial Intelligence in Marketing -- 11.3 Applications of AI in Marketing -- 11.3.1 Customer Segmentation -- 11.3.2 Analyzing Brand Associations -- Exercises -- References -- 12 AI in Supply Chain Management -- 12.1 Introduction to Supply Chain Management -- 12.1.1 Supply Chain Definition -- 12.1.2 Types of Supply Chain Models -- 12.1.3 Bullwhip Effect -- 12.1.4 Causes of Variation in Orders -- 12.1.5 Reducing the Bullwhip Effect -- 12.2 Artificial Intelligence in Supply Chain Management -- 12.3 Applications of AI in Supply Chain Management. 12.3.1 Demand Forecasting with Anomaly Detection -- 12.3.2 Quality Assurance -- 12.3.3 Estimating Delivery Time -- 12.3.4 Delivery Optimization -- Exercises -- References -- 13 AI in Operations Management -- 13.1 Introduction to Operations Management -- 13.1.1 Business Process Management -- 13.1.2 Six Sigma -- 13.1.3 Supply Chain Management (SCM) vs. Operations Management (OM) -- 13.2 Artificial Intelligence in Operations Management -- 13.3 Applications of AI in Operations -- 13.3.1 Root Cause Analysis for IT Operations -- 13.3.2 Predictive Maintenance -- 13.3.3 Process Automation -- Exercises -- References -- 14 AI in Corporate Finance -- 14.1 Introduction to Corporate Finance -- 14.2 Artificial Intelligence in Finance -- 14.3 Applications of AI in Corporate Finance -- 14.3.1 Default Prediction -- 14.3.2 Predicting Credit Card Fraud -- Exercises -- References -- 15 AI in Business Law -- 15.1 Introduction to Business Law -- 15.1.1 Types of Businesses -- 15.1.2 Types of Business Laws -- 15.2 Artificial Intelligence in Business Law -- 15.3 Applications of AI in Business Law -- 15.3.1 Legal Document Summarization -- 15.3.2 Contract Review Assistant -- 15.3.3 Legal Research Assistant -- Exercises -- References -- 16 AI in Business Strategy -- 16.1 Introduction to Business Strategy -- 16.1.1 Types of Business Strategies -- 16.1.2 Business Strategy Frameworks -- 16.1.3 Barriers to Entry -- 16.2 Artificial Intelligence in Business Strategy -- 16.3 Applications of AI in Business Strategy -- 16.3.1 Startup Acquisition -- 16.3.2 Identifying Closest Competitors -- 16.3.3 SWOT Analysis -- Exercises -- References -- Index. |
| Record Nr. | UNINA-9910767506903321 |
Teoh Teik Toe
|
||
| Singapore : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial intelligence with Python / / Teik Toe Teoh, Zheng Rong
| Artificial intelligence with Python / / Teik Toe Teoh, Zheng Rong |
| Autore | Teoh Teik Toe |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore Pte Ltd., , [2022] |
| Descrizione fisica | 1 online resource (334 pages) |
| Disciplina | 006.3 |
| Collana | Machine learning: foundations, methodologies, and applications |
| Soggetto topico |
Artificial intelligence - Data processing
Python (Computer program language) |
| ISBN |
9789811686153
9789811686146 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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. |
| Record Nr. | UNISA-996464544603316 |
|
Teoh Teik Toe
|
||
| Singapore : , : Springer Nature Singapore Pte Ltd., , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Artificial intelligence with Python / / Teik Toe Teoh, Zheng Rong
| Artificial intelligence with Python / / Teik Toe Teoh, Zheng Rong |
| Autore | Teoh Teik Toe |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore Pte Ltd., , [2022] |
| Descrizione fisica | 1 online resource (334 pages) |
| Disciplina | 006.3 |
| Collana | Machine learning: foundations, methodologies, and applications |
| Soggetto topico |
Artificial intelligence - Data processing
Python (Computer program language) |
| ISBN |
981-16-8615-7
981-16-8614-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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. |
| Record Nr. | UNISA-996549469903316 |
|
Teoh Teik Toe
|
||
| Singapore : , : Springer Nature Singapore Pte Ltd., , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Artificial intelligence with Python / / Teik Toe Teoh, Zheng Rong
| Artificial intelligence with Python / / Teik Toe Teoh, Zheng Rong |
| Autore | Teoh Teik Toe |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore Pte Ltd., , [2022] |
| Descrizione fisica | 1 online resource (334 pages) |
| Disciplina | 006.3 |
| Collana | Machine learning: foundations, methodologies, and applications |
| Soggetto topico |
Artificial intelligence - Data processing
Python (Computer program language) |
| ISBN |
981-16-8615-7
981-16-8614-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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. |
| Record Nr. | UNINA-9910743214603321 |
|
Teoh Teik Toe
|
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| Singapore : , : Springer Nature Singapore Pte Ltd., , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Convolutional neural networks for medical applications / / Teik Toe Teoh
| Convolutional neural networks for medical applications / / Teik Toe Teoh |
| Autore | Teoh Teik Toe |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore Pte Ltd., , [2023] |
| Descrizione fisica | 1 online resource (103 pages) |
| Disciplina | 616.0754 |
| Collana | SpringerBriefs in Computer Science |
| Soggetto topico |
Artificial intelligence - Data processing
Computer vision Diagnostic imaging Medicine - Data processing Neural networks (Neurobiology) |
| ISBN |
9789811988141
9789811988134 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1) Introduction -- 2) CNN for Brain Tumor classification -- 3) CNN for Pneumonia image classification -- 4) CNN for White Blood Cell classification -- 5) CNN for Skin Cancer classification -- 6) CNN for Diabetic Retinopathy detection. |
| Record Nr. | UNINA-9910683352603321 |
|
Teoh Teik Toe
|
||
| Singapore : , : Springer Nature Singapore Pte Ltd., , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Convolutional neural networks for medical applications / / Teik Toe Teoh
| Convolutional neural networks for medical applications / / Teik Toe Teoh |
| Autore | Teoh Teik Toe |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore Pte Ltd., , [2023] |
| Descrizione fisica | 1 online resource (103 pages) |
| Disciplina | 616.0754 |
| Collana | SpringerBriefs in Computer Science |
| Soggetto topico |
Artificial intelligence - Data processing
Computer vision Diagnostic imaging Medicine - Data processing Neural networks (Neurobiology) |
| ISBN |
9789811988141
9789811988134 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1) Introduction -- 2) CNN for Brain Tumor classification -- 3) CNN for Pneumonia image classification -- 4) CNN for White Blood Cell classification -- 5) CNN for Skin Cancer classification -- 6) CNN for Diabetic Retinopathy detection. |
| Record Nr. | UNISA-996546842103316 |
|
Teoh Teik Toe
|
||
| Singapore : , : Springer Nature Singapore Pte Ltd., , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||