Outros livros que poderiam interessá-lo

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  Artificial Intelligence with...

  James Ferry

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  "Artificial Intelligence with Python for Beginners: A Practical Guide to Building AI Applications" is your gateway to understanding and harnessing the power of artificial intelligence using Python. Whether you're a novice programmer, a student eager to explore the world of AI, or a professional looking to expand your skill set, this book offers a comprehensive introduction to the fundamentals of AI and practical guidance on building AI applications. 
  Starting with the basics of Python programming, you'll learn essential concepts and techniques that form the foundation of AI development. From there, you'll delve into the exciting world of machine learning, exploring supervised and unsupervised learning algorithms using popular libraries such as Scikit-learn. Through hands-on projects, you'll gain experience in data analysis, predictive modeling, and evaluation, preparing you to tackle real-world problems with confidence. 
  The journey continues with a deep dive into deep learning, where you'll discover the power of neural networks for tasks like image classification and natural language processing. Using TensorFlow and Keras, you'll build and train neural networks from scratch, mastering essential techniques for model architecture design, training, and evaluation. 
  As you progress, you'll explore advanced topics such as reinforcement learning, diving into algorithms that enable agents to learn through trial and error. Through practical projects and step-by-step tutorials, you'll develop skills in problem-solving, decision-making, and optimization, equipping you to tackle a wide range of AI challenges. 
  Throughout the book, emphasis is placed on hands-on learning, with plenty of code examples, exercises, and projects to reinforce your understanding. By the end, you'll have the knowledge and confidence to build your own AI applications, from predictive models and recommendation systems to intelligent agents and chatbots.

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  Greenhouse Planet - How Rising...

  Lewis H. Ziska

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  The carbon dioxide that industrial civilization spews into the atmosphere has dramatic consequences for life on Earth that extend beyond climate change. CO2 levels directly affect plant growth, in turn affecting any kind of life that depends on plants—in other words, everything. 
   
   
   
  Greenhouse Planet reveals the stakes of increased CO2 for plants, people, and ecosystems—from crop yields to seasonal allergies and from wildfires to biodiversity. The veteran plant biologist Lewis H. Ziska describes the importance of plants for food, medicine, and culture and explores the complex ways higher CO2 concentrations alter the systems on which humanity relies. He explains the science of how increased CO2 affects various plant species and addresses the politicization and disinformation surrounding these facts. 
   
   
   
  Ziska confronts the claim that "CO2 is plant food," a longtime conservative talking point. While not exactly false, it is deeply misleading. CO2 doesn't just make "good" plants grow; it makes all plants grow. It makes poison ivy more poisonous, kudzu more prolific, cheatgrass more flammable. CO2 stimulates some species more than others: weeds fare particularly well and become harder to control. Many crops grow more abundantly but also become less nutritious. And the further effects of climate change will be formidable.

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  Super Basic Leadership - A Guide...

  Paul D. Pantera

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  Do you want to begin your leadership journey, but you have no idea where to start? 
  There are plenty of managers in this world, but there are very few leaders. 
  To be a true leader, you need certain traits and aspects, many of which some people believe only come naturally. 
  But what if I told you that you are not innately born with leadership skills, but these skills could be taught and learned? 
  You have come to the right place. Super Basic Leadership is the perfect starting point for anyone who wants to become an excellent leader. The skills you can gain in this audiobook will teach you leadership skills that you can transfer into many different walks of life. 
  Regardless of your career or whether you want to use your leadership skills in work or your social life, this audiobook will teach you how to be the best and most effective leader possible. 
  Inside Super Basic Leadership, discover:The basics of effective leadership.Leadership roles and characteristics.The differences between leadership and authority.The value of trust in good leadership.The importance of self-discipline.Leadership challenges you may face along the way. 
  Isn’t it time for you to stop being a manager and become a leader?

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  Context - Further Selected...

  Cory Doctorow

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  One of the web's most celebrated high-tech culture mavens returns with this second collection of essays and polemics. Discussing complex topics in an accessible manner, Cory Doctorow's visions of a future where artists have full freedom of expression is tempered with his understanding that creators need to benefit from their own creations. From extolling the Etsy makerverse to excoriating Apple for dumbing down technology while creating an information monopoly, each unique piece is brief, witty, and at the cutting edge of tech. Now a stay-at-home dad as well as an international activist, Doctorow writes as eloquently about creating real-time Internet theater with his daughter as he does while lambasting the corporations that want to profit from inherent intellectual freedoms.

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  Field Guide to Edible Wild Plants

  Bradford Angier

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  Revised and updated: The classic illustrated reference for today’s foragers.   With essential information on each plant’s characteristics, distribution, and edibility, as well as updated taxonomy and eighteen new species, this is the second edition of Field Guide to Edible Wild Plants—the practical handbook for finding, preparing, and eating plants growing in the wild.   This guide to North American wild edibles has been a nature classic for over thirty years. Now David K. Foster revises Bradford Angier’s invaluable foraging reference. Scientific information for a general audience and full-color illustrations combine with intriguing accounts of the plants’ uses, making this a practical and informative resource for modern-day foragers.

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  Machine Learning System Design...

  James Ferry

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  Designing and building machine learning (ML) systems can seem daunting for beginners, but understanding the foundational steps and principles can simplify the process. At its core, ML system design involves a series of well-defined steps that guide the transformation of raw data into valuable insights through predictive models. Here’s a beginner’s guide to understanding and implementing these steps effectively. 
  The first step in designing an ML system is problem definition. Clearly defining the problem you aim to solve is crucial. This involves understanding the business context, identifying the goals, and determining the type of problem—whether it is classification, regression, clustering, or another ML task. A well-defined problem ensures that the subsequent steps are aligned with the desired outcomes. 
  Once the problem is defined, the next step is data collection and preprocessing. Data is the backbone of any ML system, and its quality significantly impacts the performance of the models. Collect data from various sources and ensure it is relevant to the problem. Data preprocessing involves cleaning the data to handle missing values, removing duplicates, and normalizing the data. It also includes feature engineering, which involves selecting, modifying, or creating new features that enhance the predictive power of the model. 
  Finally, the deployment and monitoring phase ensures that the ML model is operational and continues to perform well over time. Deploy the model to a production environment where it can make real-time predictions or be used in batch processing. Implement monitoring systems to track the model’s performance and detect any drift in data distribution that might require retraining the model. Regularly update the model with new data to maintain its accuracy and relevance. 
   

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