Stopping the training process early can also be interpreted as a form of regularization. This means that if we detected that the model started to overfit, we can stop the training process. In this case, the model will have parameters once the training has stopped.
- Title Page
- Copyright and Credits
- About Packt
- Contributors
- Preface
- Section 1: Overview of Machine Learning
- Introduction to Machine Learning with C++
- Data Processing
- Technical requirements
- Parsing data formats to C++ data structures
- Reading CSV files with the Fast-CPP-CSV-Parser library
- Preprocessing CSV files
- Reading CSV files with the Shark-ML library
- Reading CSV files with the Shogun library
- Reading CSV files with the Dlib library
- Reading JSON files with the RapidJSON library
- Writing and reading HDF5 files with the HighFive library
- Initializing matrix and tensor objects from C++ data structures
- Manipulating images with the OpenCV and Dlib libraries
- Transforming images into matrix or tensor objects of various libraries
- Normalizing data
- Summary
- Further reading
- Measuring Performance and Selecting Models
- Section 2: Machine Learning Algorithms
- Clustering
- Technical requirements
- Measuring distance in clustering
- Types of clustering algorithms
- Examples of using the Shogun library for dealing with the clustering task samples
- Examples of using the Shark-ML library for dealing with the clustering task samples
- Examples of using the Dlib library for dealing with the clustering task samples
- Plotting data with C++
- Summary
- Further reading
- Anomaly Detection
- Dimensionality Reduction
- Classification
- Recommender Systems
- Technical requirements
- An overview of recommender system algorithms 
- Non-personalized recommendations
- Content-based recommendations
- User-based collaborative filtering
- Item-based collaborative filtering
- Factorization algorithms
- Similarity or preferences correlation
- Data scaling and standardization
- Cold start problem
- Relevance of recommendations
- Assessing system quality 
- Understanding collaborative filtering method details 
- Examples of item-based collaborative filtering with C++
- Summary
- Further reading
- Ensemble Learning
- Section 3: Advanced Examples
- Neural Networks for Image Classification
- Technical requirements
- An overview of neural networks 
- Delving into convolutional networks
- What is deep learning?
- Examples of using C++ libraries to create neural networks
- Understanding image classification using the LeNet architecture
- Summary
- Further reading
- Sentiment Analysis with Recurrent Neural Networks
- Section 4: Production and Deployment Challenges
- Exporting and Importing Models
- Deploying Models on Mobile and Cloud Platforms
- Other Books You May Enjoy
Early stopping
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