Altri libri che potrebbero interessarti

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  Neural Networks for Beginners -...

  Brandon Scott

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  Embark on an enlightening journey into the fascinating world of neural networks with "Neural Networks for Beginners: A Journey Through the World of Artificial Intelligence." This comprehensive guide is meticulously crafted for absolute beginners, offering a gentle introduction to the complex and intriguing domain of artificial intelligence (AI) through the lens of neural networks. 
  With no prior knowledge required, readers are taken step by step through the foundational concepts, building blocks, and architectures that make neural networks a powerful tool in modern technology. From understanding basic terminology to exploring the architecture of various neural network models, this book demystifies AI and provides clear, engaging explanations that are accessible to all. 
  "Neural Networks for Beginners" is more than just a textbook; it's a hands-on guide that includes practical applications and project ideas, encouraging readers to apply what they learn. Through captivating case studies across diverse industries, the book showcases the real-world impact of neural networks and sparks curiosity about their potential. 
  Additionally, "Neural Networks for Beginners" provides a wealth of resources for further learning, including a glossary of key terms, a brief overview of the mathematical foundations of neural networks, and an extensive list of books, courses, websites, and communities for continued exploration. 
  Whether you're a student, professional, or simply curious about artificial intelligence, "Neural Networks for Beginners" offers an engaging and informative path to understanding and applying neural networks in the real world. Join us on this journey and unlock the potential of AI to transform the future.

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  Coding with Python - Advanced...

  Alexander Cane

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  This book will help you learn advanced level coding in Python. The first thing is to install Python. I have used Python IDLE that also is called Python shell for coding. I have also used Python text editor to write and run full programs. You can open an editor. Open the shell first and then open a new file from the file menu on the top bar. The newly opened file will appear different because it is not a shell but a text editor. Once you write a program in the editor, you can run it by clicking f5. The editor will ask you to save the file. Save it and run accordingly to get the results.  
  Python programming, just like all other types of coding, is meant to solve the problems that end-users face. To do that, you need to get additional information from the end-user. For example, if someone needs to check whether he is eligible to qualify for playing in the basketball team or whether he is qualified to vote in the election, you can create a program that can give him the right answer he needs.  
  In this kind of program, we must know the age of the user before we provide an answer, which means that we must build an interface that demands an answer from the user with respect to his or age. The user will enter his or her age in the program with the help of the input() method. Once he or she enters the age, they can know the answer. You can design your program, such as to ask them their names so that you can store their information for future reference.

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  Climate Denial

  Thomas Porcher, Henri Landes

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  Is it possible to subsidize fossil fuels on the one hand, while at the same time claiming to be committed to the fight against global warming? 
  Is it possible to promote free trade treaties throughout the world while claiming that CO2 emissions must be reduced? 
  Can we use indicators that only take into account market production without taking into account pollution?
  In view of the climate challenge, the answer to these questions should be negative, but unfortunately today it is still positive. In a documented, clear and incisive book, Thomas Porcher and Henri Landes denounce this climate hypocrisy. In the last chapter, they present 10 proposals to meet the climate challenge. 
  Instead of all the great speeches on climate, this book reminds us of the sad reality of recent years—an unprecedented development of polluting energies with unconventional gas and oil, a blind trust in non-existent solutions from the market to fight against climate change, hundreds of free trade treaties being negotiated without climate clauses, inadequate measurement indicators that encourage business as usual.

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  Hands-on NumPy for Numerical...

  Rituraj Dixit

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  Unlock the Power of NumPy to Accelerate Data Analysis and Computing.
  Book Description
  NumPy is the backbone of numerical computing in Python, powering everything from scientific research to machine learning and AI applications. Mastering NumPy is essential for anyone working with data, enabling faster computations, efficient data structures, and seamless integration with advanced analytical tools.
  Hands-on NumPy for Numerical Analysis is a comprehensive guide that takes you from the fundamentals of NumPy to its advanced applications. Through hands-on examples and real-world scenarios, this book equips data scientists, analysts, and machine learning engineers with the practical skills needed to manipulate large datasets and optimize performance. Key topics include array operations, linear algebra, signal processing, and machine learning implementations, all covered with detailed explanations and step-by-step guidance.
  Whether you're building your foundation in numerical computing or looking to enhance your data analysis workflows, this book will give you a competitive edge. Don't get left behind—harness the full power of NumPy to supercharge your data science and machine learning projects today!
  Table of Contents
  1. Getting Started with NumPy
  2. Understanding NumPy Array
  3. Data Type (dtype) in NumPy Array
  4. Indexing and Slicing in NumPy Array
  5. NumPy Array Operations
  6. NumPy Array I/O
  7. Linear Algebra with NumPy
  8. Advanced Numerical Computing
  9. Exploratory Data Analysis
  10. Performance Optimization
  11. Implementing a Machine Learning Algorithm      
  
  Index

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  Brief History of the Antarctic...

  Gerardo Bartolome

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  Antarctica, a continent like no other. Not only because of its beauty but also because of its unique history, without wars, kings or indigenous cultures. This book delves into its exploration, offering key insights into events and motivations. A concise overview, it's designed for those preparing for an Antarctic journey or seeking quick insights into its history. With over 150 images, it captures the essence of this exceptional place. For more in-depth explorations, additional resources are suggested in the final appendix. Explore Antarctica's history through a succinct and enjoyable reading experience.

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  Beating Artificial Intelligence...

  Ryan Baumgartner

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  The people who thrive alongside AI aren't the ones who fear it. They're the ones who understand it.
   
  You keep hearing that automation could make your skills obsolete, yet nobody explains the technology clearly enough for you to stay ahead of it. Every headline is either breathless hype or vague dread, and you're left wondering whether your career has a shelf life you can't quite see.
   
  That uncertainty is draining. You don't want to scramble to catch up once the change has landed. You want to see it coming and stand on the right side of it.
   
  Knowing how to future proof your career starts with actually understanding how the machines work. In this audiobook, you'll discover:
  
  The three types of artificial intelligence - what separates narrow AI from the general and super intelligence people worry about.
  Where AI really came from - the surprising origin story, from the Music Genome Project to the mathematics quietly running behind the curtain.
  Whether machines can truly learn - how something as ordinary as your spam filter reveals the entire logic of machine learning.
  The three ways machines are trained - supervised, unsupervised, and semi-supervised learning, from classification and regression to adversarial training.
  How neural networks think - the layered structure behind deep learning and why different network types suit different jobs.
  The tools that build AI - which programming languages matter and how platforms like TensorFlow put them to work.
  Where the human edge remains - the strengths machines still cannot copy, and how to lean into yours.
  
   
  You'll learn from clear, grounded explanations, including insight from computer scientist Melanie Mitchell, creator of the analogy-making program Copycat.
   
  You don't need a technical background or a single line of code to follow any of it. Understand the technology reshaping your field, and you stop reacting to the future and start preparing for it.

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