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AI and Machine Learning
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Module 1
Production-Grade Python Foundations for AI
Creating an Installable Python Project with an Isolated Environment
Create an installable Python project with an isolated environment and a `pyproject.toml` configuration.
2
Python Type Hints and Static Type Checking
Annotate functions and collections with Python type hints and validate them with a static type checker.
3
Validated Dataclasses for Model and Experiment Configuration
Represent model and experiment configuration with validated dataclasses.
4
Streaming Data with Iterators and Generators
Use iterators and generators to stream samples without loading an entire dataset into memory.
5
Reliable Resource Management with Context Managers
Use context managers to release files, devices, and other resources reliably.
6
Vectorizing Numerical Computations with NumPy Broadcasting
Replace a loop-based numerical computation with a broadcasted NumPy implementation.
7
Parametrized Unit Tests and Reusable Pytest Fixtures
Write parametrized unit tests and reusable fixtures with pytest.
8
Profiling Python Programs to Identify CPU and Memory Bottlenecks
Profile a Python program and identify its dominant CPU-time or memory bottleneck.
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