The Shark-ML library also has the functionality to define, train, and evaluate neural networks. This process can be divided into five parts: architecture definition, loss function definition, network initialization, optimizer configuration, and training.
- 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
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