فهرست مطالب

• Cover
• Title Page
• Copyright and Credits
• About Packt
• Contributors
• Table of Contents
• Preface
• Section 1: The Rationale of Machine Learning and the Usage of TensorFlow.js
• Chapter 1: Machine Learning for the Web
• Technical requirements
• Why machine learning on the web?
• Operation graphs
• What is TensorFlow.js?
• Installing TensorFlow.js
• The low-level API
• The Layers API
• Summary
• Questions
• Further reading
• Chapter 2: Importing Pretrained Models into TensorFlow.js
• Technical requirements
• The portable model format
• Exporting a model from TensorFlow
• Converting models using tfjs-converter
• Loading the model into TensorFlow.js
• Summary
• Questions
• Further reading
• Chapter 3: TensorFlow.js Ecosystem
• Technical requirements
• Why high-level libraries?
• Using existing models
• Loading the data from various kinds of storage
• Pose detection with ML5.js
• Drawing cats with Magenta.js
• XOR classification with machinelearn.js
• Summary
• Exercises
• Further reading
• Section 2: Real-World Applications of TensorFlow.js
• Chapter 4: Polynomial Regression
• Technical requirements
• What is polynomial regression?
• Two-dimensional curve fitting
• Summary
• Questions
• Further reading
• Chapter 5: Classification with Logistic Regression
• Technical requirements
• Background of binary classification
• What is logistic regression?
• Classifying two-dimensional clusters
• Summary
• Questions
• Further reading
• Chapter 6: Unsupervised Learning
• Technical requirements
• What is unsupervised learning?
• Learning how K-means works
• Generalizing K-means with the EM algorithm
• Clustering two groups in a 2D space
• Summary
• Exercise
• Further reading
• Chapter 7: Sequential Data Analysis
• Technical requirements
• What is Fourier transformation?
• Cosine curve decomposition
• Summary
• Exercise
• Further reading
• Chapter 8: Dimensionality Reduction
• Technical requirements
• Why dimensionality reduction?
• Understanding principal component analysis
• Projecting 3D points into a 2D space with PCA
• Word embedding
• Summary
• Exercise
• Further reading
• Chapter 9: Solving the Markov Decision Process
• Technical requirements
• Reinforcement learning
• Solving the four-states environment
• Summary
• Exercise
• Further reading
• Section 3: Productionizing Machine Learning Applications with TensorFlow.js
• Chapter 10: Deploying Machine Learning Applications
• Technical requirements
• The ecosystem around the JavaScript platform
• Module bundler
• Deploying modules with GitHub Pages
• Summary
• Questions
• Further reading
• Chapter 11: Tuning Applications to Achieve High Performance
• Technical requirements
• The backend API of TensorFlow.js
• Tensor management
• Asynchronous data access
• Profiling
• Model visualization
• Summary
• Questions
• Further reading
• Chapter 12: Future Work Around TensorFlow.js
• Technical requirements
• Experimental backend implementations
• AutoML edge helper
• Summary
• Questions
• Further Reading
• Other Books You May Enjoy
• Index