A value is any thing in the form of a number, string, character, etc. that can be manipulated. Values can also be referred to as objects. 

Values in Python

Operators are things that represent computational processes in Python. These include +, -, *, **, //, %, and /. 
- "+" = Addition
- "-" = Subtraction
- "*" = Multiplication

- "**" = Exponent
- "//" = Divide, but only include whole number in result
- "%" = Modulo, which only displays remainder of operands 

Operands are the values being acted on by the operator. 

Operators & Operands in Python

Function can be thought of as devices that take in material, perform a specified operation on the taken-in material, then output a resulting object. 

Inputs are referred to as arguments 
End result is referred to as output or return value. 

Functions in Python

A statement is any line of instruction that the Python interpreter can potentially execute. 

Types of statements include but are not limited to:
- Variable assignment statements 
- while statements
- for statements
- if statements
- import statements 

Statements in Python 

- Expressions are any combination of data types to be evaluated. Evaluating an expression results in values/objects. Expressions can also be evaluated into or set to values. 
- Asking Python to print an expression will result in the interpreter attempting to evaluate the expression and display the result

Expressions in Python 

- General Tips & Tricks
- Common Syntax Errors
- Error types
- Error Messages

Debugging in Python

A Class allows us to group together data and functions. It provides a template which can be instantiated to create objects. Classes and Objects help improve readability and allow code reuse. 

Classes are most often used in Object Oriented Programming, as they allow programmers to bundle sets of functions and data together. 

Python Classes and Objects

Data structures facilitate the organization of data in computer memory in an efficient manner which further support faster and more flexible data manipulations.


The python interpreter offers the following built-in data structures:


- List
- Set
- Tuple
- Dictionary


Data Structures in Python

https://www.geeksforgeeks.org/vectorization-in-python/

Vectorization in python

Vectorization is a computational technique used to replace explicit, element-by-element loops (such as Python for-loops) with optimized operations on entire data structures simultaneously. In machine learning, vectorization leverages highly optimized linear algebra libraries to perform mathematical operations—like calculating the elementwise sum of vectors or processing whole minibatches of examples—at once. This approach dramatically reduces execution time by yielding order-of-magnitude speedups, simplifies code, reduces the potential for bugs, and increases portability by offloading mathematics to underlying libraries.

Vectorization

- Values 
- Operators & Operands
- Functions
- Statements
- Expressions
- Debugging
- Classes and Objects
- Data Structures

University of Michigan - Ann Arbor

San Jose State University

Python is an interpreted, object-oriented, high-level programming language. It's known for its built in data structures and robust libraries. Despite this, Python was developed to have an easy to learn syntax which emphasizes readability and whitespace. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and standard library are available without charge for all major platforms, and can be freely distributed.

Python Programming Language

https://runestone.academy/runestone/books/published/fopp/index.html

Runestone: Foundations of Python Programming

- Coursera Python Specializations
- Runestone Python 3 Course 
- Python for Everybody MOOC 
- W3Schools Python Tutorial
- Python Wiki
- PEP 8 Code Style Guide
- PyPI

Python Programming Language References

Python Programming Language Foundational Concepts 

Python is a powerful language that allows data scientists to perform data manipulation, analysis, and visualizations through its vast number of libraries such as numpy and pandas.

Data Science with Python

Python is widely used for machine learning various reasons.
- It is relatively easy to learn.
- The data handling capacity is better than most other languages.
- The community is large, as well as support for other platforms.
- Its’s Open Source.
- It supports developers with frameworks and libraries such as TensorFlow


Machine Learning with Python

- Virtual Environments
- Package Management
- Python and the Web
- Concurrency and Paralellism
- Deep Learning
- Quantum Computing
- GUI Development
- Popular Tools for Python
- Computer Vision (OpenCV)
- Sorting Algorithms
- Image Processing

Python Programming Advanced Topics

Python is an interpreted language, meaning that code written in the language is not converted into machine-readable code, but instead byte code. This byte code is then translated by an interpreter called the Python Virtual Machine whenever the program runs. On the fly, the interpreter translates the byte code into machine-readable into code that the CPU executes.

Interpretation

Python is an object-oriented programming language. OOP is a programming paradigm that provides a means of structuring programs so that properties and behaviors are bundled into individual objects. For instance, an object could represent a person with properties like a name, age, and address and behaviors such as walking, talking, breathing, and running. Object-oriented programming is an approach for modeling concrete, real-world things, like cars, as well as relations between things, like companies and employees, students and teachers, and so on. OOP models real-world entities as software objects that have some data associated with them and can perform certain functions.

Python as an Object Oriented Programming (OOP) Language

Imperative programming is a programming paradigm where computation is executed step-by-step as it is defined in the code. This approach makes it easier to design new machine learning models because developers can intuitively utilize standard control flow constructs (such as loops and conditionals) and tap into vast, existing software ecosystems, like that of Python. Additionally, imperative programming simplifies the debugging process, as developers can readily access and inspect all intermediate variable values during execution.

Imperative Programming

Conventionally, when evaluating deep neural networks, the Python interpreter must execute the code layer by layer to generate instructions that are then forwarded to a CPU or GPU. While this eager execution is fine for a single fast device, the single-threaded nature of the Python interpreter becomes a severe bottleneck when scaling to advanced multi-GPU servers (e.g., an 8-GPU instance). In such setups, Python struggles to generate instructions fast enough to keep all GPUs fully utilized. This bottleneck can be resolved by using hybrid programming to compile sequential models into optimized symbolic representations, thereby bypassing the Python interpreter during execution.

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