Recommended Books on Python - 20% OFF! with code: COMPUTING20
Python 3 The Comprehensive Guide
A detailed guide to developing proficiency in Python > GO!
"Python 3 The Comprehensive Guide" is an extensive reference book designed for both beginners and experienced programmers aiming to master Python. The book offers a deep dive into the language’s core concepts, advanced techniques, and practical applications, making it a robust resource for learning and reference.

Overview:

  • Comprehensive Coverage: Spanning 1036 pages, the guide covers everything from basic Python syntax and fundamental programming principles to more sophisticated topics such as GUIs, network programming, debugging, and optimization.
  • Audience: Ideal for beginners who need to grasp Python fundamentals as well as for professionals seeking to deepen their understanding of advanced programming concepts.
  • Hands-On Approach: The book includes plenty of code examples and downloadable supplements, ensuring readers can practice and implement what they learn.

Key Features:

  • Complete Python 3 Handbook: Covers the entirety of Python 3 including functions, methods, data types, modularization, and object orientation.
  • Advanced Topics: Explores more advanced areas such as graphical user interfaces (GUIs), network communication, debugging techniques, and working with frameworks like Django.
  • Practical Code Examples: Offers downloadable code samples that help solidify the concepts and allow for hands-on practice.
  • Flexible Format: Available in multiple formats including a DRM-free e-book (EPUB and PDF) and a print edition, catering to different reading preferences.
  • Author Expertise: Written by Johannes Ernesti and Peter Kaiser, both of whom are research scientists with strong backgrounds in mathematics, computer science, and robotics.
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Getting Started with Python
Learn to create your own programs from scratch > GO!
"Getting Started with Python" is a beginner-friendly guide designed to introduce new programmers to the fundamentals of Python programming. Authored by Thomas Theis, this book offers a practical, step-by-step approach to learning Python, making it an ideal resource for those just beginning their coding journey.

Overview:

  • Target Audience: Beginners who want to learn Python from scratch.
  • Format: Available as both an e-book and a print edition.
  • Length & Publication: 437 pages, published in 2024.

Key Features:

  • Language Basics: Covers installation, running your first program, and understanding core Python elements such as variables, operators, loops, and branching.
  • Python in Practice: Introduces object-oriented programming, error handling, and working with various data types. Demonstrates practical applications like file processing, multithreading, and database integration (using SQLite and MySQL).
  • GUI Programming: Provides guidance on developing graphical user interfaces, with specific mention of using PyQt.
  • Example Exercises: Includes hands-on exercises, such as a game development example, to help solidify understanding and build real-world applications.
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Generative AI with Python
Master generative artificial intelligence in Python > GO!
"Generative AI with Python" is a comprehensive guide aimed at developers looking to harness the power of generative AI using Python. Scheduled for release on May 28, 2025, this 475-page book by Bert Gollnick provides both theoretical foundations and practical, hands-on examples.

Overview:

  • Target Audience: Developers and data scientists eager to build generative AI applications.
  • Format & Availability: Offered as an e-book and print edition, available for pre-order.
  • Content Focus: Combines foundational AI concepts with advanced techniques, including practical coding examples and downloadable code.

Key Features:

  • Large Language Models (LLMs): Learn to set up and work with pretrained LLMs and NLP models using popular tools such as OpenAI, Meta’s Llama, and various open-source alternatives. The book guides you through prompt engineering, working with chains, and leveraging these models in Python.
  • Vector Databases: Discover how to create and manage vector databases to store and query large collections of documents. The book explains the complete pipeline from document processing to efficient retrieval.
  • Retrieval Augmented Generation (RAG): Understand how to integrate external knowledge sources with pretrained models to enhance text generation. Detailed instructions help you retrieve relevant information and fuse it into the generation process for more informed outputs.
  • Agentic Systems: Explore the development of autonomous AI agents using Python libraries and frameworks like crewAI, AutoGen, and LangChain. This section covers how to build systems where AI models can make decisions and act independently.
  • Hands-On Code Examples: The guide comes with downloadable code, enabling you to practice and implement the concepts in real-world scenarios.
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Python for Engineering and Scientific Computing
Utilize the Python ecosystem for scientific analysis > GO!
"Python for Engineering and Scientific Computing" is a hands-on guide tailored for engineers, scientists, and technical professionals who want to leverage Python to solve complex computational problems. This book not only covers the fundamentals of Python programming but also dives deep into its application in engineering and scientific contexts.

Overview:

  • Target Audience: Ideal for engineers, researchers, and scientists looking to integrate Python into their problem-solving toolkit.
  • Format & Details: Available in both e-book and print editions, offering a comprehensive mix of theoretical insights and practical examples.

Key Features:

  • Solid Python Foundation: Introduces core Python concepts such as syntax, data structures, control flow, and functions, making it accessible for beginners while serving as a useful reference for experienced users.
  • Scientific Libraries: Focuses on essential Python libraries for numerical and scientific computing, including:
      NumPy: For efficient numerical operations.
      SciPy: For advanced scientific computations.
      Matplotlib: For data visualization and graphical representation.
  • Real-World Applications: Provides practical examples and case studies that demonstrate how Python can be used to model, simulate, and solve real-world engineering and scientific problems.
  • Hands-On Exercises & Code Samples: Reinforces learning with practical exercises and downloadable code, enabling readers to apply the techniques directly to their projects.
  • Comprehensive Reference Material: Serves as both an instructional guide and a reference manual, making it a valuable resource for continuous learning and professional development in technical fields.
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Programming Neural Networks with Python
Leverage the potential of neural networks > GO!
"Programming Neural Networks with Python" is a practical guide designed for developers, data scientists, and AI enthusiasts who want to build and understand neural network models using Python. The book combines theoretical insights with hands-on programming examples to help readers implement neural networks effectively in real-world applications.

Overview:

  • Target Audience: Ideal for developers, researchers, and practitioners interested in machine learning and neural networks.
  • Format & Availability: Available in both e-book and print editions, complete with downloadable code samples for practical learning.

Key Features:

  • Fundamental Concepts: Introduces core neural network principles including network architectures, activation functions, and the backpropagation algorithm.
  • Hands-On Implementation: Provides step-by-step tutorials and real-world examples to guide you through designing, training, and evaluating neural network models using Python.
  • Utilization of Python Libraries: Leverages popular Python libraries and frameworks to simplify the development process, enabling you to implement sophisticated neural network models efficiently.
  • Optimization and Troubleshooting: Offers insights into tuning network parameters, improving model performance, and addressing common challenges in neural network programming.
  • Practical Applications: Demonstrates how neural networks can be applied across various domains, such as image recognition, natural language processing, and predictive analytics.
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Developing AI Applications
Create AI applications tailored to your needs > GO!
"Developing AI Applications" is a practical, beginner-friendly guide by Metin Karatas designed to help you create your own AI solutions—even if you have little or no coding experience.

Overview:

  • Target Audience: Geared towards individuals who are new to coding and AI, this book is ideal for those who want to move beyond pre-built chatbots and plug-and-play solutions to build custom AI applications.
  • Format & Publication: The guide is available in both e-book and print editions, offering 402 pages of hands-on content, sample data, code, and Jupyter notebooks.

Key Features:

  • Fundamental AI Algorithms: Learn the theory and practical applications behind AI methods such as decision trees, convolutional layers, cluster analysis, anomaly detection, and reinforcement learning.
  • Tools and Platforms: Get started with no-code AI using platforms like KNIME. Transition to programming-based development using basic Python, along with popular tools such as ChatGPT, DALL-E, AutoKeras, and Anaconda.
  • Practical, Step-by-Step Exercises: Follow clear, guided exercises that help you build your own AI applications from scratch. The book includes sample data and downloadable code to reinforce learning through practice.
  • Accessible for Beginners: Designed to be approachable even if you have never coded before, with gradual progression from fundamental concepts to more complex applications.
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