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MATLAB Machine Learning [[electronic resource] /] / by Michael Paluszek, Stephanie Thomas
| MATLAB Machine Learning [[electronic resource] /] / by Michael Paluszek, Stephanie Thomas |
| Autore | Paluszek Michael |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2017 |
| Descrizione fisica | 1 online resource (XIX, 326 p. 140 illus., 74 illus. in color.) |
| Disciplina | 006 |
| Soggetto topico |
Artificial intelligence
Programming languages (Electronic computers) Computer programming Artificial Intelligence Programming Languages, Compilers, Interpreters Programming Techniques |
| ISBN | 1-4842-2250-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 Overview of Machine Learning -- 2 The History of Machine Learning -- 3 Software for machine learning -- 4 Representation of data for Machine Learning in MATLAB -- 5 MATLAB Graphics -- 6 Machine Learning Examples in MATLAB -- 7 Face Recognition with Deep Learning -- 8 Data Classification -- 9 Classification of Numbers Using Neural Networks -- 10 Kalman Filters -- 11 Adaptive Control -- 12 Autonomous Driving -- Bibliography. |
| Record Nr. | UNINA-9910157379403321 |
Paluszek Michael
|
||
| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
MATLAB Machine Learning Recipes : A Problem-Solution Approach / / by Michael Paluszek, Stephanie Thomas
| MATLAB Machine Learning Recipes : A Problem-Solution Approach / / by Michael Paluszek, Stephanie Thomas |
| Autore | Paluszek Michael |
| Edizione | [3rd ed. 2024.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2024 |
| Descrizione fisica | 1 online resource (458 pages) |
| Disciplina | 006.31 |
| Soggetto topico |
Artificial intelligence
Big data Artificial Intelligence Big Data |
| ISBN |
1484298462
9781484298466 1484298454 9781484298459 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. An Overview of Machine Learning -- Chapter 2. Data Representation -- Chapter 3. MATLAB Graphics -- Chapter 4. Kalman Filters -- Chapter 5. Adaptive Control -- Chapter 6. Neural Aircraft Control -- Chapter 7. Fuzzy Logic -- Chapter 8. Classification with Neural Nets -- Chapter 9. Simple Neural Nets -- Chapter 10. Data Classification. - Chapter 11. Neural Nets with Deep Learning -- Chapter 12. Multiple Hypothesis Testing -- Chapter 13. Autonomous Driving with MHT -- Chapter 14. Case-Based Expert Systems -- Chapter 15. Spacecraft Attitude Determination Using Neural Nets. -Appendix A Brief History of Autonomous Learning -- Appendix B. Software for Autonomous Learning. |
| Record Nr. | UNINA-9910842282703321 |
|
Paluszek Michael
|
||
| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
MATLAB Machine Learning Recipes : A Problem-Solution Approach / / by Michael Paluszek, Stephanie Thomas
| MATLAB Machine Learning Recipes : A Problem-Solution Approach / / by Michael Paluszek, Stephanie Thomas |
| Autore | Paluszek Michael |
| Edizione | [2nd ed. 2019.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2019 |
| Descrizione fisica | 1 online resource (358 pages) |
| Disciplina | 006.3 |
| Soggetto topico |
Artificial intelligence
Big data Artificial Intelligence Big Data |
| ISBN |
1-5231-5037-8
1-4842-3916-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 Overview -- 2 Data Representation -- 3 MATLAB Graphics -- 4 Kalman Filters -- 5 Adaptive Control -- 6 Fuzzy Logic -- 7 Data Classification with Decision Trees -- 8 Simple Neural Nets -- 9 Classification with Neural Nets -- 10 Neural Nets with Deep Learning -- 11 Neural Aircraft Control -- 12 Multiple Hypothesis Testing -- 13 Autonomous Driving with MHT -- 14 Case-Based Expert Systems -- Appendix A: A Brief History of Autonomous Learning -- Appendix B: Software for Machine Learning. |
| Record Nr. | UNINA-9910338008103321 |
|
Paluszek Michael
|
||
| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
MATLAB recipes : a problem-solution approach / / Michael Paluszek, Stephanie Thomas
| MATLAB recipes : a problem-solution approach / / Michael Paluszek, Stephanie Thomas |
| Autore | Paluszek Michael |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | [Place of publication not identified] : , : Apress, , [2020] |
| Descrizione fisica | 1 online resource (XIX, 415 p. 140 illus., 125 illus. in color.) |
| Disciplina | 001.6420151 |
| Soggetto topico | Numerical analysis - Computer programs |
| ISBN | 1-4842-6124-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part I: Coding in MATLAB -- 1: Coding Handbook -- 2: MATLAB Style -- 3: Visualization -- 4: Interactive Graphics -- 5: Testing and Debugging -- 6: Classes -- Part II: Applications -- 7: The Double Integrator -- 8: Robotics -- 9: Electric Motors -- 10: Fault Detection -- 11: Chemical Processes -- 12: Aircraft -- 13: Spacecraft Attitude Control -- 14: Automobiles. |
| Record Nr. | UNINA-9910440647003321 |
|
Paluszek Michael
|
||
| [Place of publication not identified] : , : Apress, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
MATLAB recipes [[electronic resource] ] : a problem-solution approach / / by Michael Paluszek, Stephanie Thomas
| MATLAB recipes [[electronic resource] ] : a problem-solution approach / / by Michael Paluszek, Stephanie Thomas |
| Autore | Paluszek Michael |
| Edizione | [1st ed. 2015.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2015 |
| Descrizione fisica | 1 online resource (316 p.) |
| Disciplina | 510 |
| Collana | Expert's Voice in MATLAB |
| Soggetto topico |
Programming languages (Electronic computers)
Computer software Programming Languages, Compilers, Interpreters Mathematical Software |
| ISBN | 1-4842-0559-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part I: Coding in MATLAB -- 1. Coding Basics -- 2. MATLAB Style -- 3. Visualization -- 4. Interactive Graphics -- 5. Testing and Debugging -- Part II: Applications -- 6. The Double Integrator -- 7. Robotics -- 8. Electrical Motor -- 9. Fault Detection -- 10. Chemical Processes -- 11. Aircraft -- 12. Spacecraft.-. |
| Record Nr. | UNINA-9910300642603321 |
|
Paluszek Michael
|
||
| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Practical MATLAB deep learning : a projects-based approach / / Michael Paluszek, Stephanie Thomas, and Eric Ham
| Practical MATLAB deep learning : a projects-based approach / / Michael Paluszek, Stephanie Thomas, and Eric Ham |
| Autore | Paluszek Michael |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | New York, New York : , : Apress Media LLC, , [2022] |
| Descrizione fisica | 1 online resource (338 pages) |
| Disciplina | 006.31 |
| Collana | ITpro collection |
| Soggetto topico | Machine learning |
| ISBN | 1-4842-7912-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Contents -- About the Authors -- About the Technical Reviewer -- Acknowledgments -- Preface to the Second Edition -- 1 What Is Deep Learning? -- 1.1 Deep Learning -- 1.2 History of Deep Learning -- 1.3 Neural Nets -- 1.3.1 Daylight Detector -- Problem -- Solution -- How It Works -- 1.3.2 XOR Neural Net -- Problem -- Solution -- How It Works -- 1.4 Deep Learning and Data -- 1.5 Types of Deep Learning -- 1.5.1 Multi-layer Neural Network -- 1.5.2 Convolutional Neural Network (CNN) -- 1.5.3 Recurrent Neural Network (RNN) -- 1.5.4 Long Short-Term Memory Network (LSTM) -- 1.5.5 Recursive Neural Network -- 1.5.6 Temporal Convolutional Machine (TCM) -- 1.5.7 Stacked Autoencoders -- 1.5.8 Extreme Learning Machine (ELM) -- 1.5.9 Recursive Deep Learning -- 1.5.10 Generative Deep Learning -- 1.5.11 Reinforcement Learning -- 1.6 Applications of Deep Learning -- 1.7 Organization of the Book -- 2 MATLAB Toolboxes -- 2.1 Commercial MATLAB Software -- 2.1.1 MathWorks Products -- Deep Learning Toolbox -- Instrument Control Toolbox -- Statistics and Machine Learning Toolbox -- Computer Vision Toolbox -- Image Acquisition Toolbox -- Parallel Computing Toolbox -- Text Analytics Toolbox -- 2.2 MATLAB Open Source -- 2.3 XOR Example -- 2.4 Training -- 2.5 Zermelo's Problem -- 3 Finding Circles -- 3.1 Introduction -- 3.2 Structure -- 3.2.1 imageInputLayer -- 3.2.2 convolution2dLayer -- 3.2.3 batchNormalizationLayer -- 3.2.4 reluLayer -- 3.2.5 maxPooling2dLayer -- 3.2.6 fullyConnectedLayer -- 3.2.7 softmaxLayer -- 3.2.8 classificationLayer -- 3.2.9 Structuring the Layers -- 3.3 Generating Data -- 3.3.1 Problem -- 3.3.2 Solution -- 3.3.3 How It Works -- 3.4 Training and Testing -- 3.4.1 Problem -- 3.4.2 Solution -- 3.4.3 How It Works -- 4 Classifying Movies -- 4.1 Introduction -- 4.2 Generating a Movie Database -- 4.2.1 Problem -- 4.2.2 Solution.
4.2.3 How It Works -- 4.3 Generating a Viewer Database -- 4.3.1 Problem -- 4.3.2 Solution -- 4.3.3 How It Works -- 4.4 Training and Testing -- 4.4.1 Problem -- 4.4.2 Solution -- 4.4.3 How It Works -- 5 Algorithmic Deep Learning -- 5.1 Building the Filter -- 5.1.1 Problem -- 5.1.2 Solution -- 5.1.3 How It Works -- 5.2 Simulating -- 5.2.1 Problem -- 5.2.2 Solution -- 5.2.3 How It Works -- 5.3 Testing and Training -- 5.3.1 Problem -- 5.3.2 Solution -- 5.3.3 How It Works -- 6 Tokamak Disruption Detection -- 6.1 Introduction -- 6.2 Numerical Model -- 6.2.1 Dynamics -- 6.2.2 Sensors -- 6.2.3 Disturbances -- 6.2.4 Controller -- 6.3 Dynamical Model -- 6.3.1 Problem -- 6.3.2 Solution -- 6.3.3 How It Works -- 6.4 Simulate the Plasma -- 6.4.1 Problem -- 6.4.2 Solution -- 6.4.3 How It Works -- 6.5 Control the Plasma -- 6.5.1 Problem -- 6.5.2 Solution -- 6.5.3 How It Works -- 6.6 Training and Testing -- 6.6.1 Problem -- 6.6.2 Solution -- 6.6.3 How It Works -- 7 Classifying a Pirouette -- 7.1 Introduction -- 7.1.1 Inertial Measurement Unit -- 7.1.2 Physics -- 7.2 Data Acquisition -- 7.2.1 Problem -- 7.2.2 Solution -- 7.2.3 How It Works -- 7.3 Orientation -- 7.3.1 Problem -- 7.3.2 Solution -- 7.3.3 How It Works -- 7.4 Dancer Simulation -- 7.4.1 Problem -- 7.4.2 Solution -- 7.4.3 How It Works -- 7.5 Real-Time Plotting -- 7.5.1 Problem -- 7.5.2 Solution -- 7.5.3 How It Works -- 7.6 Quaternion Display -- 7.6.1 Problem -- 7.6.2 Solution -- 7.6.3 How It Works -- 7.7 Making the IMU Belt -- 7.7.1 Problem -- 7.7.2 Solution -- 7.7.3 How It Works -- 7.8 Testing the System -- 7.8.1 Problem -- 7.8.2 Solution -- 7.8.3 How It Works -- 7.9 Classifying the Pirouette -- 7.9.1 Problem -- 7.9.2 Solution -- 7.9.3 How It Works -- 7.10 Data Acquisition GUI -- 7.10.1 Problem -- 7.10.2 Solution -- 7.10.3 How It Works -- 7.11 Hardware Sources -- 8 Completing Sentences -- 8.1 Introduction. 8.1.1 Sentence Completion -- 8.1.2 Grammar -- 8.1.3 Sentence Completion by Pattern Recognition -- 8.1.4 Sentence Generation -- 8.2 Generating a Database -- 8.2.1 Problem -- 8.2.2 Solution -- 8.2.3 How It Works -- 8.3 Creating a Numeric Dictionary -- 8.3.1 Problem -- 8.3.2 Solution -- 8.3.3 How It Works -- 8.4 Mapping Sentences to Numbers -- 8.4.1 Problem -- 8.4.2 Solution -- 8.4.3 How It Works -- 8.5 Converting the Sentences -- 8.5.1 Problem -- 8.5.2 Solution -- 8.5.3 How It Works -- 8.6 Training and Testing -- 8.6.1 Problem -- 8.6.2 Solution -- 8.6.3 How It Works -- 9 Terrain-Based Navigation -- 9.1 Introduction -- 9.2 Modeling Our Aircraft -- 9.2.1 Problem -- 9.2.2 Solution -- 9.2.3 How It Works -- 9.3 Generating Terrain -- 9.3.1 Problem -- 9.3.2 Solution -- 9.3.3 How It Works -- 9.4 Close-Up Terrain -- 9.4.1 Problem -- 9.4.2 Solution -- 9.4.3 How It Works -- 9.5 Building the Camera Model -- 9.5.1 Problem -- 9.5.2 Solution -- 9.5.3 How It Works -- 9.6 Plotting the Trajectory -- 9.6.1 Problem -- 9.6.2 Solution -- 9.6.3 How It Works -- 9.7 Creating the Training Images -- 9.7.1 Problem -- 9.7.2 Solution -- 9.7.3 How It Works -- 9.8 Training and Testing -- 9.8.1 Problem -- 9.8.2 Solution -- 9.8.3 How It Works -- 9.9 Simulation -- 9.9.1 Problem -- 9.9.2 Solution -- 9.9.3 How It Works -- 10 Stock Prediction -- 10.1 Introduction -- 10.2 Generating a Stock Market -- 10.2.1 Problem -- 10.2.2 Solution -- 10.2.3 How It Works -- 10.3 Creating a Stock Market -- 10.3.1 Problem -- 10.3.2 Solution -- 10.3.3 How It Works -- 10.4 Training and Testing -- 10.4.1 Problem -- 10.4.2 Solution -- 10.4.3 How It Works -- 11 Image Classification -- 11.1 Introduction -- 11.2 Using AlexNet -- 11.2.1 Problem -- 11.2.2 Solution -- 11.2.3 How It Works -- 11.3 Using GoogLeNet -- 11.3.1 Problem -- 11.3.2 Solution -- 11.3.3 How It Works -- 12 Orbit Determination -- 12.1 Introduction. 12.2 Generating the Orbits -- 12.2.1 Problem -- 12.2.2 Solution -- 12.2.3 How It Works -- 12.3 Training and Testing -- 12.3.1 Problem -- 12.3.2 Solution -- 12.3.3 How It Works -- 12.4 Implementing an LSTM -- 12.4.1 Problem -- 12.4.2 Solution -- 12.4.3 How It Works -- 13 Earth Sensors -- 13.1 Introduction -- 13.2 Linear Output Earth Sensor -- 13.2.1 Problem -- 13.2.2 Solution -- 13.2.3 How It Works -- 13.3 Segmented Earth Sensor -- 13.3.1 Problem -- 13.3.2 Solution -- 13.3.3 How It Works -- 13.4 Linear Output Sensor Neural Network -- 13.4.1 Problem -- 13.4.2 Solution -- 13.4.3 How It Works -- 13.5 Segmented Sensor Neural Network -- 13.5.1 Problem -- 13.5.2 Solution -- 13.5.3 How It Works -- 14 Generative Modeling of Music -- 14.1 Introduction -- 14.2 Generative Modeling Description -- 14.3 Problem: Music Generation -- 14.4 Solution -- 14.5 Implementation -- 14.6 Alternative Methods -- 15 Reinforcement Learning -- 15.1 Introduction -- 15.2 Titan Lander -- 15.3 Titan Atmosphere -- 15.3.1 Problem -- 15.3.2 Solution -- 15.3.3 How It Works -- 15.4 Simulating the Aircraft -- 15.4.1 Problem -- 15.4.2 Solution -- 15.4.3 How It Works -- 15.5 Simulating Level Flight -- 15.5.1 Problem -- 15.5.2 Solution -- 15.5.3 How It Works -- 15.6 Optimal Trajectory -- 15.6.1 Problem -- 15.6.2 Solution -- 15.6.3 How It Works -- 15.7 Reinforcement Example -- 15.7.1 Problem -- 15.7.2 Solution -- 15.7.3 How It Works -- Bibliography -- Index. |
| Record Nr. | UNINA-9910592983203321 |
|
Paluszek Michael
|
||
| New York, New York : , : Apress Media LLC, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Practical MATLAB Deep Learning : A Project-Based Approach / / by Michael Paluszek, Stephanie Thomas
| Practical MATLAB Deep Learning : A Project-Based Approach / / by Michael Paluszek, Stephanie Thomas |
| Autore | Paluszek Michael |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2020 |
| Descrizione fisica | 1 online resource (XV, 252 p. 111 illus., 100 illus. in color.) |
| Disciplina | 005.13 |
| Soggetto topico |
Programming languages (Electronic computers)
Artificial intelligence Computer input-output equipment Computer science—Mathematics Computer programming Programming Languages, Compilers, Interpreters Artificial Intelligence Hardware and Maker Mathematics of Computing Programming Techniques |
| ISBN | 1-4842-5124-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 What is Deep Learning? -- 2 MATLAB Machine and Deep Learning Toolboxes -- 3 Finding Circles with Deep Learning -- 4 Classifying Movies -- 5 Algorithmic Deep Learning -- 6 Tokamak Disruption Detection -- 7 Classifying a Pirouette -- 8 Completing Sentences -- 9 Terrain Based Navigation -- 10 Stock Prediction -- 11 Image Classification -- 12 Orbit Determination. |
| Record Nr. | UNINA-9910380733303321 |
|
Paluszek Michael
|
||
| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||