MATLAB for Machine Learning Unlock the Power of Deep Learning for Swift and Enhanced Results
Master MATLAB tools for creating machine learning applications through effective code writing, guided by practical examples showcasing the versatility of machine learning in real-world applications Key Features Work with the MATLAB Machine Learning Toolbox to implement a variety of machine learning...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt Publishing
[2024]
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| Edición: | Second edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009799143606719 |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright and Credits
- Contributors
- Table of Contents
- Preface
- Part 1: Getting Started with Matlab
- Chapter 1: Exploring MATLAB for Machine Learning
- Technical requirements
- Introducing ML
- How to define ML
- Analysis of logical reasoning
- Learning strategy typologies
- Discovering the different types of learning processes
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Semi-supervised learning
- Transfer learning
- Using ML techniques
- Selecting the ML paradigm
- Step-by-step guide on how to build ML models
- Exploring MATLAB toolboxes for ML
- Statistics and Machine Learning Toolbox
- Deep Learning Toolbox
- Reinforcement Learning Toolbox
- Computer Vision Toolbox
- Text Analytics Toolbox
- ML applications in real life
- Summary
- Chapter 2: Working with Data in MATLAB
- Technical requirements
- Importing data into MATLAB
- Exploring the Import Tool
- Using the load() function to import files
- Reading ASCII-delimited files
- Exporting data from MATLAB
- Working with different types of data
- Working with images
- Audio data handling
- Exploring data wrangling
- Introducing data cleaning
- Discovering exploratory statistics
- EDA
- EDA in practice
- Introducing exploratory visualization
- Understanding advanced data preprocessing techniques in MATLAB
- Data normalization for feature scaling
- Introducing correlation analysis in MATLAB
- Summary
- Part 2: Understanding Machine Learning Algorithms in MATLAB
- Chapter 3: Prediction Using Classification and Regression
- Technical requirements
- Introducing classification methods using MATLAB
- Decision trees for decision-making
- Exploring decision trees in MATLAB
- Building an effective and accurate classifier
- SVMs explained
- Supervised classification using SVM.
- Exploring different types of regression
- Introducing linear regression
- Linear regression model in MATLAB
- Making predictions with regression analysis in MATLAB
- Multiple linear regression with categorical predictor
- Evaluating model performance
- Reducing outlier effects
- Using advanced techniques for model evaluation and selection in MATLAB
- Understanding k-fold cross-validation
- Exploring leave-one-out cross-validation
- Introducing the bootstrap method
- Summary
- Chapter 4: Clustering Analysis and Dimensionality Reduction
- Technical requirements
- Understanding clustering - basic concepts and methods
- How to measure similarity
- How to find centroids and centers
- How to define a grouping
- Understanding hierarchical clustering
- Partitioning-based clustering algorithms with MATLAB
- Introducing the k-means algorithm
- Using k-means in MATLAB
- Grouping data using the similarity measures
- Applying k-medoids in MATLAB
- Discovering dimensionality reduction techniques
- Introducing feature selection methods
- Exploring feature extraction algorithms
- Feature selection and feature extraction using MATLAB
- Stepwise regression for feature selection
- Carrying out PCA
- Summary
- Chapter 5: Introducing Artificial Neural Network Modeling
- Technical requirements
- Getting started with ANNs
- Basic concepts relating to ANNs
- Understanding how perceptrons work
- Activation function to introduce non-linearity
- ANN's architecture explained
- Training and testing an ANN model in MATLAB
- How to train an ANN
- Introducing the MATLAB Neural Network Toolbox
- Understanding data fitting with ANNs
- Discovering pattern recognition using ANNs
- Building a clustering application with an ANN
- Exploring advanced optimization techniques
- Understanding SGD
- Exploring Adam optimization.
- Introducing second-order methods
- Summary
- Chapter 6: Deep Learning and Convolutional Neural Networks
- Technical requirements
- Understanding DL basic concepts
- Automated feature extraction
- Training a DNN
- Exploring DL models
- Approaching CNNs
- Convolutional layer
- Pooling layer
- ReLUs
- FC layer
- Building a CNN in MATLAB
- Exploring the model's results
- Discovering DL architectures
- Understanding RNNs
- Analyzing LSTM networks
- Introducing transformer models
- Summary
- Part 3: Machine Learning in Practice
- Chapter 7: Natural Language Processing Using MATLAB
- Technical requirements
- Explaining NLP
- NLA
- NLG
- Analyzing NLP tasks
- Introducing automatic processing
- Exploring corpora and word and sentence tokenizers
- Corpora
- Words
- Sentence tokenize
- Implementing a MATLAB model to label sentences
- Introducing sentiment analysis
- Movie review sentiment analysis
- Using an LSTM model for label sentences
- Understanding gradient boosting techniques
- Approaching ensemble learning
- Bagging definition and meaning
- Discovering random forest
- Boosting algorithms explained
- Summary
- Chapter 8: MATLAB for Image Processing and Computer Vision
- Technical requirements
- Introducing image processing and computer vision
- Understanding image processing
- Explaining computer vision
- Exploring MATLAB tools for computer vision
- Building a MATLAB model for object recognition
- Introducing handwriting recognition (HWR)
- Training and fine-tuning pretrained deep learning models in MATLAB
- Introducing the ResNet pretrained network
- The MATLAB Deep Network Designer app
- Interpreting and explaining machine learning models
- Understanding saliency maps
- Understanding feature importance scores
- Discovering gradient-based attribution methods
- Summary.
- Chapter 9: Time Series Analysis and Forecasting with MATLAB
- Technical requirements
- Exploring the basic concepts of time series data
- Understanding predictive forecasting
- Introducing forecasting methodologies
- Time series analysis
- Extracting statistics from sequential data
- Converting a dataset into a time series format in MATLAB
- Understanding time series slicing
- Resampling time series data in MATLAB
- Moving average
- Exponential smoothing
- Implementing a model to predict the stock market
- Dealing with imbalanced datasets in MATLAB
- Understanding oversampling
- Exploring undersampling
- Summary
- Chapter 10: MATLAB Tools for Recommender Systems
- Technical requirements
- Introducing the basic concepts of recommender systems
- Understanding CF
- Content-based filtering explained
- Hybrid recommender systems
- Finding similar users in data
- Creating recommender systems for network intrusion detection using MATLAB
- Recommender system for NIDS
- NIDS using a recommender system in MATLAB
- Deploying machine learning models
- Understanding model compression
- Discovering model pruning techniques
- Introducing quantization for efficient inference on edge devices
- Getting started with knowledge distillation
- Learning low-rank approximation
- Summary
- Chapter 11: Anomaly Detection in MATLAB
- Technical requirements
- Introducing anomaly detection and fault diagnosis systems
- Anomaly detection overview
- Fault diagnosis systems explained
- Approaching fault diagnosis using ML
- Using ML to identify anomalous functioning
- Anomaly detection using logistic regression
- Improving accuracy using the Random Forest algorithm
- Building a fault diagnosis system using MATLAB
- Understanding advanced regularization techniques
- Understanding dropout
- Exploring L1 and L2 regularization.
- Introducing early stopping
- Summary
- Index
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