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AI and LLM Development
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Module 1
Python for Accelerated AI Development
1
Set Up a Reproducible PyTorch GPU Environment on Windows or WSL
Create a reproducible Python environment on Windows or WSL and verify that PyTorch can use the laptop's NVIDIA GPU.
Create a reproducible Python environment on Windows or WSL and verify that PyTorch can use the laptop's NVIDIA GPU.
2
Translating Control Flow and Functions into Typed Python
Translate familiar control-flow and function patterns into Python using type hints.
Translate familiar control-flow and function patterns into Python using type hints.
3
Python Sequences and Mappings: Slicing, Unpacking, and Comprehensions
Manipulate Python sequences and mappings using slicing, unpacking, and comprehensions.
Manipulate Python sequences and mappings using slicing, unpacking, and comprehensions.
4
Classes, Modules, and Exception Handling
Define classes and organize reusable code into importable modules with exception handling.
Define classes and organize reusable code into importable modules with exception handling.
5
Memory-Efficient Data Processing with Iterators and Generators
Use iterators and generators to process data without loading an entire dataset into memory.
Use iterators and generators to process data without loading an entire dataset into memory.
6
NumPy Indexing, Vectorization, and Broadcasting
Apply NumPy indexing, vectorization, and broadcasting to numerical arrays.
Apply NumPy indexing, vectorization, and broadcasting to numerical arrays.
7
Loading, Cleaning, and Serializing Text Data with Python
Load, clean, and serialize a small text dataset using standard Python data tools.
Load, clean, and serialize a small text dataset using standard Python data tools.
8
Writing Pytest Unit Tests for Data Processing and Numerical Code
Write unit tests with pytest for data-processing and numerical code.
Write unit tests with pytest for data-processing and numerical code.
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