Book-Giveaway thread two by Developer Nation

I would really love to get The official Go programming language book by Adam Donovan and Brian Kernighan to learn this summer

I am urgently seeking a copy of “Fundamentals of Data Engineering” by Joe Reis and Matt Housley to enhance my understanding of data systems. This book is crucial for my studies and projects.

Here is the link to the book: https://a.co/d/2LZcZDu.

Thank you for being so considerate.

Best regards,

Abhijeet T.

Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter, Third Edition - HARDCOVER

Hello Vanessa,

I sincerely appreciate this opportunity to participate in the book giveaway. I am keenly interested in receiving a copy of “Learning Salesforce Development with Apex” (Hard Copy) by Paul Battisson. This book would greatly contribute to my knowledge and expertise in Salesforce development, allowing me to enhance my skills in this crucial area.

Thank you for considering my request.

Best regards,
Nivetha

1 Like

Hello everyone, I would really love to get The official Go programming language book by Adam Donovan and Brian Kernighan. Learning golang is a dream of mine.

Hi

I won a book how do I claim?

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Third Edition (Full Colour Print) - HARDCOVER

这是一本很实用的书籍。

I’d recommend “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. This book is excellent for engineers interested in AI for several reasons:

  1. Comprehensive coverage: It provides a thorough introduction to deep learning, covering both the theoretical foundations and practical applications.

  2. Written by experts: The authors are renowned researchers in the field, ensuring high-quality, up-to-date content.

  3. Bridges theory and practice: The book balances mathematical concepts with practical implementation details, making it valuable for both understanding and applying deep learning techniques.

  4. Broad scope: It covers various neural network architectures and their applications, giving readers a wide perspective on the field.

  5. Accessible yet rigorous: While it doesn’t shy away from technical details, the book is written in a way that’s accessible to those with a solid mathematical and programming background.

This book can serve as both an introduction to deep learning for engineers new to AI and a reference for more experienced practitioners.