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    AI Programming
    AI Programming
    AI Programming
    Ebook94 pages56 minutes

    AI Programming

    By Alisa Turing and AI

    ()

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    About this ebook

    "AI Programming" offers a comprehensive roadmap for developers seeking to bridge the gap between theoretical artificial intelligence concepts and practical implementation. This guide strategically combines foundational AI knowledge with hands-on programming experience, focusing on popular frameworks like TensorFlow and PyTorch while maintaining accessibility for readers with intermediate programming skills.



    The book takes a unique dual-track approach, progressing from essential Python prerequisites through advanced topics like deep learning and model deployment. Rather than dwelling purely on theory, it provides working code examples, case studies, and performance benchmarks that demonstrate real-world applications. Each chapter builds upon previous concepts, moving from AI fundamentals and framework architecture to implementing common models and optimization strategies for production environments.



    What sets this resource apart is its practical focus combined with thorough explanations of underlying concepts. While mathematical concepts are addressed, the book doesn't require advanced mathematics knowledge, making it particularly valuable for software developers transitioning to AI development. Through annotated code samples, debugging guides, and hands-on projects, readers learn to build neural networks, implement AI algorithms, and optimize their applications while understanding the reasoning behind specific implementation choices. The balanced approach to frameworks like TensorFlow and PyTorch provides readers with the knowledge to make informed decisions for their own projects.

    LanguageEnglish
    PublisherPublifye
    Release dateJan 13, 2025
    ISBN9788233946579
    AI Programming

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      Book preview

      AI Programming - Alisa Turing

      Python Programming Essentials for AI Development

      In the bustling landscape of artificial intelligence, Python has emerged as the undisputed lingua franca. Like a master key that unlocks countless doors, Python's elegance and versatility have made it the go-to language for AI developers worldwide. But what makes this seemingly simple language so powerful in the realm of artificial intelligence?

      The Foundation: Python Basics for AI

      Before we dive into the intricate world of AI programming, let's establish our foundational building blocks. Python's syntax, often praised for its readability, mirrors English-like statements. This isn't a coincidence – it was designed with clarity in mind, making it an ideal choice for expressing complex AI algorithms.

      Did You Know? Python was named after the British comedy group Monty Python, not the snake! This quirky origin reflects the language's philosophy of making programming fun and accessible.

      Data Structures: The Building Blocks of AI

      In AI development, data is our currency, and Python provides robust structures to manage it efficiently. Lists, dictionaries, sets, and tuples form the backbone of data manipulation. Think of these structures as different types of containers – each with its specific purpose in the AI ecosystem.

      Lists: Ordered sequences perfect for storing training data

      Dictionaries: Key-value pairs ideal for feature mapping

      Sets: Unique collections useful for filtering duplicate data

      Tuples: Immutable sequences for storing fixed parameters

      NumPy: The Numerical Powerhouse

      Enter NumPy, the numerical computing library that transforms Python from a general-purpose language into a scientific computing powerhouse. Its array operations are blazingly fast, making it indispensable for AI applications.

      NumPy arrays can perform operations up to 50 times faster than traditional Python lists, making them crucial for large-scale AI computations.

      Pandas: Data Manipulation Made Easy

      If NumPy is the engine, Pandas is the sophisticated transmission system of our AI vehicle. It provides powerful data structures and operations for manipulating numerical tables and time series. The DataFrame object, Pandas' primary data structure, handles complex operations with remarkable ease.

      Object-Oriented Programming in AI

      In AI development, we often need to create reusable, modular code. Object-oriented programming (OOP) principles in Python allow us to build complex AI systems while maintaining clean, organized code. Classes become blueprints for AI models, methods become training procedures, and inheritance helps us build upon existing algorithms.

      Did You Know? The most popular deep learning frameworks like TensorFlow and PyTorch are built using Python's OOP principles, allowing developers to create complex neural networks with just a few lines of code.

      Functions and Lambda Expressions

      Functions in Python act as the workhorses of AI development. They encapsulate complex operations and make our code modular and reusable. Lambda expressions, Python's way of creating small anonymous functions, are particularly useful in data preprocessing and feature engineering.

      Essential Libraries for AI Development

      SciPy: Advanced computing and optimization

      Scikit-learn: Machine learning algorithms and tools

      Matplotlib: Data visualization and plotting

      TensorFlow/PyTorch: Deep learning frameworks

      Code Patterns in AI

      Certain code patterns appear frequently in AI development. The pipeline pattern, for instance, is crucial for data preprocessing. The factory pattern helps in creating different types of AI models, while the observer pattern is useful in training

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