Neuroanatomia - texto e atlas

This book provides a bridge between the worlds of Python 3 programming and Generative AI, aiming to equip readers with the skills to navigate both domains with confidence. It begins with an introduction to fundamental aspects of Python programming, which include various data types, number formatting, Unicode and UTF-8 handling, and text manipulation techniques. In addition, you will learn about loops, functions, data structures, NumPy, Pandas, conditional logic, and reserved words in Python. Further chapters show how to handle user input, manage exceptions, and work with command-line arguments. The text then transitions to the realm of Generative AI, discussing its distinction from Conversational AI. Popular platforms and models, including Bard (now called “Gemini”) and its competitors, are presented to give readers an understanding of the current AI landscape. The book discusses the capabilities of Bard, its strengths, weaknesses, and potential applications. Finally, you will learn how to generate a variety of Python 3 code samples via Bard. Cover Title Page Copyright Page Dedication Contents Preface Chapter 1: Introduction to Python 3 Tools for Python easy_install and pip virtualenv IPython Python Installation Setting the PATH Environment Variable (Windows Only) Launching Python on Your Machine The Python Interactive Interpreter Python Identifiers Lines, Indentation, and Multilines Quotation and Comments in Python Saving Your Code in a Module Some Standard Modules in Python The help() and dir() Functions Compile Time and Runtime Code Checking Simple Data Types in Python Working With Numbers Working With Other Bases The chr() Function The round() Function in Python Formatting Numbers in Python Working With Fractions Unicode and UTF-8 Working With Unicode Working With Strings Comparing Strings Formatting Strings in Python Slicing and Splicing Strings Testing for Digits and Alphabetic Characters Search and Replace a String in Other Strings Remove Leading and Trailing Characters Printing Text without NewLine Characters Text Alignment Working With Dates Converting Strings to Dates Exception Handling in Python Handling User Input Command-Line Arguments Summary Chapter 2: Conditional Logic, Loops, and Functions Precedence of Operators in Python Python Reserved Words Working with Loops in Python Python for Loops A for Loop with try/except in Python Numeric Exponents in Python Nested Loops The split() Function With for Loops Using the split() Function to Compare Words Using the split() Function to Print Justified Text Using the split() Function to Print Fixed-Width Text Using the split() Function to Compare Text Strings Using the split() Function to Display Characters in a String The join() Function Python while Loops Conditional Logic in Python The break/continue/pass Statements Comparison and Boolean Operators The in/not in/is/is not Comparison Operators The and, or, and not Boolean Operators Local and Global Variables Uninitialized Variables and the Value None Scope of Variables Pass by Reference Versus Value Arguments and Parameters Using a while loop to Find the Divisors of a Number Using a while loop to Find Prime Numbers User-Defined Functions in Python Specifying Default Values in a Function Returning Multiple Values From a Function Functions With a Variable Number of Arguments Lambda Expressions Recursion Calculating Factorial Values Calculating Fibonacci Numbers Calculating the GCD of Two Numbers Calculating the LCM of Two Numbers Summary Chapter 3: Python Data Structures Working with Lists Lists and Basic Operations Reversing and Sorting a List Lists and Arithmetic Operations Lists and Filter-Related Operations Sorting Lists of Numbers and Strings Expressions in Lists Concatenating a List of Words The Bubble Sort in Python The Python range() Function Counting Digits and Uppercase and Lowercase Letters Arrays and the append() Function Working with Lists and the split() Function Counting Words in a List Iterating Through Pairs of Lists Other List-Related Functions Using a List as a Stack and a Queue Working with Vectors Working with Matrices The NumPy Library for Matrices Queues Tuples (Immutable Lists) Sets Dictionaries Creating a Dictionary Displaying the Contents of a Dictionary Checking for Keys in a Dictionary Deleting Keys from a Dictionary Iterating Through a Dictionary Interpolating Data from a Dictionary Dictionary Functions and Methods Dictionary Formatting Ordered Dictionaries Sorting Dictionaries Python Multi Dictionaries Other Sequence Types in Python Mutable and Immutable Types in Python The type() Function Working with Bard Counting Digits and Uppercase and Lowercase Letters Bard Python Code for a Queue Bard Python Code for a Stack Summary Chapter 4: Introduction to NumPy and Pandas What is NumPy? Useful NumPy Features What are NumPy arrays? Working with Loops Appending Elements to Arrays (1) Appending Elements to Arrays (2) Multiply Lists and Arrays Doubling the Elements in a List Lists and Exponents Arrays and Exponents Math Operations and Arrays Working with “-1” Subranges with Vectors Working with “–1” Subranges with Arrays Other Useful NumPy Methods Arrays and Vector Operations NumPy and Dot Products (1) NumPy and Dot Products (2) NumPy and the “Norm” of Vectors NumPy and Other Operations NumPy and the reshape() Method Calculating the Mean and Standard Deviation Calculating Quartiles With Numpy What is Pandas? Pandas Data Frames DataFrames and Data Cleaning Tasks A Labeled Pandas DataFrame Pandas Numeric DataFrames Pandas Boolean DataFrames Transposing a Pandas DataFrame Pandas DataFrames and Random Numbers Combining Pandas DataFrames (1) Combining Pandas DataFrames (2) Data Manipulation with Pandas DataFrames (1) Data Manipulation with Pandas DataFrames (2) Data Manipulation with Pandas DataFrames (3) Pandas DataFrames and CSV Files Pandas DataFrames and Excel Spreadsheets Select, Add, and Delete Columns in DataFrames Pandas DataFrames and Scatterplots Pandas DataFrames and Simple Statistics Useful One-Line Commands in Pandas Working with Bard A Pandas DataFrame with Random Values Pandas DataFrame and a Bar Chart Pandas DataFrames and Statistics Summary Chapter 5: Generative AI, Bard, and Gemini What is Generative AI? Key Features of Generative AI Popular Techniques in Generative AI What Makes Generative AI Unique Conversational AI Versus Generative AI Primary Objective Applications Technologies Used Training and Interaction Evaluation Data Requirements Is Gemini Part of Generative AI? DeepMind DeepMind and Games Player of Games (PoG) OpenAI Cohere Hugging Face Hugging Face Libraries Hugging Face Model Hub AI21 InflectionAI Anthropic What is Prompt Engineering? Prompts and Completions Types of Prompts Instruction Prompts Reverse Prompts System Prompts Versus Agent Prompts Prompt Templates Poorly-Worded Prompts What is Gemini? Gemini Ultra Versus GPT-4 Gemini Strengths Gemini’s Weaknesses Gemini Nano on Mobile Devices What is Bard? Sample Queries and Responses from Bard Alternatives to Bard YouChat Pi from Inflection CoPilot (OpenAI/Microsoft) Codex (OpenAI) Apple GPT Claude 2 Summary Chapter 6: Bard and Python Code CSV Files for Bard Simple Web Scraping Basic Chatbot Basic Data Visualization Basic Pandas Generating Random Data Recursion: Fibonacci Numbers Generating a Python Class Asynchronous Programming Working with Requests in Python Image Processing with PIL Exception Handling Generators in Python Roll 7 or 11 with Two Dice Roll 7 or 11 with Three Dice Roll 7 or 11 with Four Dice Mean and Standard Deviation Summary Index