Weitere Bücher, die Sie mögen werden

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  Data Science for Beginners - A...

  Daniel Garfield

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  In an age where data reigns supreme, harnessing the power of information has become a paramount skill. "Data Science for Beginners" is your key to unlocking the secrets of data and delving into the captivating world of analytics. Whether you're a curious novice or an aspiring data enthusiast, this book offers a comprehensive and accessible introduction to the dynamic field of data science. 
  Embark on a journey of discovery as you uncover the fundamental principles and techniques that underpin data science. From understanding the different types of data and data formats to exploring the iterative data science process, you'll gain a solid foundation to navigate the vast sea of data with confidence. 
  Dive deep into the art of data acquisition and cleaning, learning how to gather and refine datasets from diverse sources. Discover the art of exploratory data analysis (EDA) through the lens of captivating visualizations, as you unravel hidden patterns, correlations, and trends within the data. 
  Delve into the heart of statistical analysis, mastering descriptive and inferential statistics to draw meaningful insights from data. Embrace the power of machine learning and its practical applications, from supervised learning for classification and regression to unsupervised learning for clustering and dimensionality reduction. 
  With a focus on ethical considerations, you'll explore the importance of data privacy, security, and fairness in the realm of data science. Discover how to evaluate model performance, ensuring the accuracy and reliability of machine learning algorithms. 
  Let "Data Science for Beginners" be your guiding light into the captivating realm of data exploration. 
   

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  A Field Guide to Climate Anxiety...

  Sarah Jaquette Ray

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  Gen Z's first "existential toolkit" for combating eco-guilt and burnout while advocating for climate justice. 
   
   
   
  A youth movement is reenergizing global environmental activism. The "climate generation"—late millennials and iGen, or Generation Z—is demanding that policy makers and government leaders take immediate action to address the dire outcomes predicted by climate science. Those inheriting our planet's environmental problems expect to encounter challenges, but they may not have the skills to grapple with the feelings of powerlessness and despair that may arise when they confront this seemingly intractable situation. 
   
   
    
  Drawing on a decade of experience leading and teaching in college environmental studies programs, Sarah Jaquette Ray has created an "existential tool kit" for the climate generation. Combining insights from psychology, sociology, social movements, mindfulness, and the environmental humanities, Ray explains why and how we need to let go of eco-guilt, resist burnout, and cultivate resilience while advocating for climate justice. A Field Guide to Climate Anxiety is the essential guidebook for the climate generation—and perhaps the rest of us—as we confront the greatest environmental threat of our time.

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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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  Domain-specific LLMs -...

  Et Tu Code

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  This audiobook is a comprehensive guide to leveraging Language Models (LLMs) tailored for specific domains. Here's what each section offers: 
  Introduction to Domain-specific LLMs: Lay the groundwork by understanding the fundamentals of Domain-specific LLMs 
  Key Concepts in Domain-specific LLMs: Delve deeper into core principles like domain adaptation, fine-tuning, and transfer learning, 
  Creating Custom LLMs: Learn to build custom Language Models tailored to specific domains 
  Steps to Create LLMs: Follow a roadmap for creating Domain-specific Language Models, from data acquisition to model training and evaluation. 
  Popular LLMs: Explore popular models like BERT, GPT, and XLNet customized for specific industries, comparing features and performance. 
  Fine-tuning LLMs: Master techniques for optimizing model performance and adapting to new tasks in specialized domains. 
  Integration with Systems: Discover best practices for integrating LLMs into existing systems and workflows seamlessly. 
  Resources and Tools: Access a curated collection of resources, datasets, and tools to support LLM development. 
  Pre-trained Models: Harness pre-trained models to expedite development and enhance efficiency in LLM projects. 
  Security Considerations: Address security concerns related to LLM deployment, ensuring robustness and integrity. 
  Hugging Face LLMs: Discover Hugging Face's Domain-specific LLMs platform for accessing pre-trained models. 
  Whether you're a researcher, developer, data scientist, or industry professional, "Domain-specific LLMs" equips you with the knowledge, skills, and insights to harness the full potential of Language Models in specialized domains.

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  Renewable energy finance: Green...

  International Renewable Energy...

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  Renewables have emerged as a major recipient of green bond proceeds. This brief highlights the need for more green bonds to drive sustainable, climate safe, energy finance.

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  How to Make Money Online with AI...

  Brian Scott Fitzgerald

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  Do you want to Make Money Online with AI even if you are a beginner? 
    
   “How to Make Money with AI” is your comprehensive guide for optimizing AI and growing your business. Even if you have never used AI before, this book will show you how to set up your AI account, become a top online earner, and create passive income sources. 
    
  Benefits of reading this book: 
    
  - Discover how to use AI to create engaging content, market products, and increase productivity 
  - Learn how to optimize business processes and boost marketing campaigns to increase sales 
  - Leverage AI to create unique solutions and generate passive income 
  - Over 3000 Prompts to help you make even more money online 
  - Get a Free Book….. “Beyond ChatGPT and 50 New AI Tools 
    
    
  This game-changing guide will give you the tools to take full advantage of this cutting-edge AI technology. Following the detailed instructions, you can quickly develop passive income sources, produce high-quality work quickly and easily, and publish engaging content. 
    
  What’s included in this book: 
    
  - Over 100 new ways to help you make even more money online with AI. 
  - 50 of the newest, most popular AI Tools. 
  - Tips for creating and marketing online courses 
  - Strategies for optimizing customer service 
  - Ideas for leveraging AI to generate passive income 
    
  Buy "How to Make Money with AI” now to start making money online with ChatGPT! And don’t forget the other Free Book. “Beyond ChatGPT”

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