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Scalable Data Science with Python
1. Parallel Computing Basics
1.1. Modern Computer Architecture
1.2. Serial Execution v.s. Parallel Execution
1.3. Threads and Processes
1.4. Parallel Programming Design Methods
1.5. Performance Metrics
2. Data Science
2.1. Data Science Lifecycle
2.2. Machine Learning
2.3. Deep Learning
2.4. Hyperparameter Optimization
2.5. Ecosystem and Content
3. Dask
3.1. Dask Overview
3.2. Getting Started with Dask DataFrame
3.3. Scaling Dask to a Cluster
3.4. GPU
3.5. Task Graph and Data Partitioning
4. Dask DataFrame
4.1. Reading and Writing Data
4.2. Indexing
4.3.
map_partitions
4.4. Shuffle
4.5. Data Analysis with Dask
5. Machine Learning with Dask
5.1. Data Preprocessing
5.2. Hyperparameter Tuning
5.3. Distributed Machine Learning
6. Ray
6.1. Ray Overview
6.2. Ray Remote Functions
6.3. Distributed Object Storage
6.4. Ray Remote Classes
7. Ray cluster
7.1. Ray Cluster
7.2. Computing resources and resource groups
7.3. Ray Job
8. Ray Data
8.1. Ray Data Overview
8.2. Data Loading, Inspection, and Saving
8.3. Data Transformation
8.4. Preprocessor
8.5. Modin
9. Ray Machine Learning
9.1. Ray Train
9.2. Ray Tune
9.3. Ray Serve
10. Xorbits
10.1. Xorbits Data
10.2. Xinference
11. MPI for Python
11.1. MPI Overview
11.2. MPI Hello World
11.3. Point-to-Point Communication
11.4. Collective Communication
11.5. Remote Memory Access
12. References
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