Advanced Python Programming for Machine Learning
Chapter 1: Advanced Python Constructs for ML Pipelines
Advanced Generator Techniques for Memory-Efficient Data Handling
Context Managers for Resource Management in ML Workflows
Functional Programming Patterns in Python for Data Transformation
Higher-Order Functions and Closures in ML
Working with Iterators and Itertools for Complex Sequences
Hands-on Practical: Building a Data Pipeline Component
Chapter 2: Performance Optimization in Python for ML
Profiling Python Code: Identifying Bottlenecks
Optimizing NumPy Operations
Efficient Pandas Usage for Large Datasets
Introduction to Cython for Speeding Up Python Code
Using Numba for Just-In-Time Compilation
Understanding Python's Global Interpreter Lock (GIL)
Memory Profiling and Optimization Techniques
Hands-on Practical: Optimizing a Feature Engineering Function
Chapter 3: Metaprogramming and Python Internals for ML Frameworks
Advanced Decorator Applications
Understanding and Implementing Descriptors
Metaclasses: Customizing Class Creation
Dynamic Code Generation and Execution
Introspection and Reflection Techniques
Attribute Access Customization (__getattr__, __getattribute__)
Hands-on Practical: Building a Plugin System with Metaclasses
Chapter 4: Advanced Data Structures and Algorithms in Python for ML
Implementing Trees for Hierarchical Data
Graph Data Structures and Algorithms
Using Priority Queues and Heaps
Spatial Data Structures (Quadtrees, Octrees)
Probabilistic Data Structures (Bloom Filters, HyperLogLog)
Algorithm Design Paradigms (Greedy, Dynamic Programming) in ML
Hands-on Practical: Implementing a k-d Tree for Nearest Neighbor Search
Chapter 5: Concurrency and Parallelism in Python for ML Workloads
Threading vs Multiprocessing for ML Tasks
The multiprocessing Module for Parallel Execution
Inter-Process Communication (IPC) Techniques
Using concurrent.futures for High-Level Concurrency
Introduction to asyncio for Asynchronous ML Operations
Synchronization Primitives (Locks, Semaphores, Events)
Debugging Concurrent Python Applications
Hands-on Practical: Parallelizing Data Preprocessing
Chapter 6: Building Custom ML Estimators and Transformers with Python
Scikit-learn API and Estimator Interface
Implementing Custom Transformers
Developing Custom Estimators
Composition and Inheritance for ML Components
Parameter Validation and Management
Integrating Custom Components into Pipelines
Testing Custom ML Components
Hands-on Practical: Building a Custom Ensemble Estimator
Probabilistic Data Structures (Bloom Filters, HyperLogLog)
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- Space/time trade-offs in hash coding with allowable errors, Burton H. Bloom, 1970 Communications of the ACM, Vol. 13 (Association for Computing Machinery (ACM)) DOI: 10.1145/362686.362692 - Original paper introducing the Bloom filter, a probabilistic data structure for set membership testing.
- HyperLogLog: the analysis of a near-optimal cardinality estimation algorithm, Philippe Flajolet, Éric Fusy, Olivier Gandouet, Frédéric Meunier, 2007 Discrete Mathematics & Theoretical Computer Science, DMTCS Proceedings vol. AH, 2007 Conference on Analysis of Algorithms (AofA 07), Vol. AH (DMTCS) DOI: 10.46298/dmtcs.3545 - The foundational paper presenting the HyperLogLog algorithm for highly efficient approximate cardinality estimation.
- Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeff Ullman, 2020 (Cambridge University Press) - A comprehensive textbook that covers various data mining algorithms, including detailed sections on Bloom filters and HyperLogLog, relevant for large-scale data applications. 3rd edition.