Hands On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Artificial Intelligence
Book Details
Book Title
Hands On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Author
Aurélien Géron
Publisher
O'Reilly Media
Publication Date
2022
ISBN
1098125975
Number of Pages
1351
Language
English
Format
File Size
11MB
Subject
Machine Learning , Deep Learning
Table of Contents
- Preface
- I. The Fundamentals of Machine Learning
- 1. The Machine Learning Landscape
- What Is Machine Learning?
- Why Use Machine Learning?
- Examples of Applications
- Types of Machine Learning Systems
- Main Challenges of Machine Learning
- Testing and Validating
- Exercises
- 2. End-to-End Machine Learning Project
- Working with Real Data
- Look at the Big Picture
- Get the Data
- Explore and Visualize the Data to Gain Insights
- Prepare the Data for Machine Learning Algorithms
- Select and Train a Model
- Fine-Tune Your Model
- Launch, Monitor, and Maintain Your System
- Try It Out!
- Exercises
- 3. Classification
- MNIST
- Training a Binary Classifier
- Performance Measures
- Multiclass Classification
- Error Analysis
- Multilabel Classification
- Multioutput Classification
- Exercises
- 4. Training Models
- Linear Regression
- Gradient Descent
- Polynomial Regression
- Learning Curves
- Regularized Linear Models
- Logistic Regression
- Exercises
- 5. Support Vector Machines
- Linear SVM Classification
- Nonlinear SVM Classification
- SVM Regression
- Under the Hood of Linear SVM Classifiers
- The Dual Problem
- Exercises
- 6. Decision Trees
- Training and Visualizing a Decision Tree
- Making Predictions
- Estimating Class Probabilities
- The CART Training Algorithm
- Computational Complexity
- Gini Impurity or Entropy?
- Regularization Hyperparameters
- Regression
- Sensitivity to Axis Orientation
- Decision Trees Have a High Variance
- Exercises
- 7. Ensemble Learning and Random Forests
- Voting Classifiers
- Bagging and Pasting
- Random Forests
- Boosting
- Stacking
- Exercises
- 8. Dimensionality Reduction
- The Curse of Dimensionality
- Main Approaches for Dimensionality Reduction
- PCA
- Random Projection
- LLE
- Other Dimensionality Reduction Techniques
- Exercises
- 9. Unsupervised Learning Techniques
- Clustering Algorithms: k-means and DBSCAN
- Gaussian Mixtures
- Exercises
- II. Neural Networks and Deep Learning
- 10. Introduction to Artificial Neural Networks with Keras
- From Biological to Artificial Neurons
- Implementing MLPs with Keras
- Fine-Tuning Neural Network Hyperparameters
- Exercises