Otros libros que te pueden interesar

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  Quantum Software Development for...

  Chuck Sherman

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  Are you fascinated by the limitless potential of quantum computing and its implications for the world of machine learning? Quantum Machine Learning for Beginners is your key to unlocking the secrets of this groundbreaking technology and harnessing its power from the ground up. 
  In this accessible and comprehensive guide, we'll take you on a journey through the exciting realms of quantum computing and machine learning. Whether you're a total novice or have a basic understanding of these fields, this book is designed to help you build a solid foundation and venture into the fascinating world of quantum machine learning with confidence. 
  Quantum Machine Learning for Beginners is a resource that welcomes both curious beginners and budding quantum enthusiasts, offering a solid introduction to the fascinating intersection of quantum computing and machine learning. Whether you're a student, a developer, or a tech enthusiast, this book equips you with the understanding and skills to embark on a quantum machine learning journey. With clear explanations, illustrative examples, and hands-on exercises, you'll be prepared to unlock the infinite possibilities of quantum technology and contribute to the future of this exciting field. 
  Embark on your quantum machine learning journey today by grabbing your copy of Quantum Machine Learning for Beginners! 
   

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  The Hidden Systems That Quietly...

  Owen Blackwood

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  This audiobook is narrated by an AI Voice.  
  Expose the invisible structures, algorithms, and institutional mechanisms that shape your choices, opportunities, and life trajectory without your awareness or consent. This revealing audiobook examines how credit scores, recommendation algorithms, insurance calculations, and bureaucratic classifications determine more about your life than personal decisions ever could. Learn how systems designed for efficiency create outcomes no one intended while remaining invisible to those affected by their judgments. The narrator reveals the inner workings of systems that decide what information you see, what opportunities you receive, what prices you pay, and how institutions treat you based on data you never knew was collected. Discover how systemic biases embedded in algorithmic decision-making perpetuate discrimination while appearing objective and neutral. Understand why individual effort cannot overcome systemic obstacles that most people do not even recognize as structural rather than personal failures. This audiobook draws from technology researchers, sociologists, and investigative journalists exposing how systems consolidate power while preventing accountability. Learn to identify systemic influences on your life and develop strategies for navigating systems designed without your interests in mind. Essential listening for anyone wanting to understand why life outcomes often seem disconnected from effort and merit, and what forces actually determine success in modern society.

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  Machine Learning with Python -...

  Alexander Cane

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  Ways to combat retraining depend on the algorithm and consist of the correct values of the trainer's met parameters. In practice, model estimation is not performed on the same input data that was used to train the model. Divide 10-20% of all available data into a separate set and call it a set for evaluation. We will bring the other 10-20% into the set for ratification, and 60-80% of the remaining ones will be given to the trainer. The principle of data sharing depends on the data and the task. Random sampling is often a good method if inputs are independent of each other, and there is no strong imbalance between the number of positive and negative entries. 
  The intuitive analogy here is the same as with university studies: the teacher solves some problems with students in pairs and gives other’s similar tasks in the exam. What is important here (both in teaching students and models) is that these tasks are varied, and students cannot simply memorize the answers, and those who have mastered the material (similar tasks) will be able to repeat the thought process and answer correctly. 
  In machine learning, we split into data two sets: we will use the evaluation set to evaluate each model we train, using different approaches, algorithms, and model types to select the best one. That is, for each model, we will have two precision values - precision on the training dataset and precision on the evaluation dataset. It is normal for the former to be higher than the second, but not significantly. A big difference indicates retraining.

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  SLEEP CODE - Soothing Sounds for...

  Sleep Sound Productions LLC

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  ***** Soothing Sounds for Sleep and Relaxation: 20 Non-Looping Tracks *****Drift into a peaceful slumber with this therapeutic collection of 20 unique, non-looping soundscapes, created to transform your bedtime into a calming retreat.SLEEP CODE offers a complete listening experience with no repetition or abrupt loops, keeping you immersed in serenity from beginning to end. Gentle tones and calming atmospheres weave together to create a sanctuary of rest, inviting body and mind into quiet stillness.Whether you're seeking relief from insomnia, a tranquil backdrop for mindfulness, or a way to ease racing thoughts late at night, SLEEP CODE soundscapes provide the perfect companion for sleep, meditation, and deep relaxation.Ready to unwind? Close your eyes, press play, and let these soothing sounds guide you gently into a night of peaceful, restorative rest.

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  Mastering Neural Networks - A...

  Et Tu Code

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  "Mastering Neural Networks" is the ultimate guide for anyone eager to delve into the realm of neural networks. With a comprehensive approach, this audiobook navigates through the intricacies of neural networks, offering a deep understanding coupled with practical implementation techniques. 
  Beginning with the fundamentals, listeners are introduced to the core concepts of neural networks, exploring their architecture and mechanisms. From there, it delves into the building blocks, paving the way for a thorough comprehension of feedforward, convolutional, and recurrent neural networks.  
  But this audiobook doesn't stop at theory. It takes listeners on a journey through practical application, offering step-by-step guidance on creating neural networks using Python. Through popular examples and real-world applications, listeners gain insight into the diverse applications of neural networks, from image recognition to natural language processing. 
  Yet, "Mastering Neural Networks" goes beyond mere application. It explores optimization techniques, ethical considerations, and interpretability, ensuring that listeners not only build effective neural networks but also deploy them responsibly. From neuroevolution to neuromorphic computing, it provides a glimpse into the cutting-edge advancements shaping the future of artificial intelligence. 
  Whether you're a beginner seeking a solid foundation or a seasoned practitioner aiming to deepen your understanding, "Mastering Neural Networks" equips you with the knowledge and tools to navigate the dynamic landscape of neural networks with confidence and proficiency.

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  African Artificial Intelligence...

  Mark Nasila

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  Artificial intelligence (AI) is upending life, work, and play as we know it, and it's only just getting started. The rise of AI is a milestone on par with the discovery of fire, the invention of the wheel, and the creation of the internet. In short, AI is going to change everything. For some, that's an exciting prospect. For others, it's terrifying. However you feel about AI, there's no escaping it, whether you're in a global metropolis or a farmer in rural KwaZulu-Natal.
  Dr Mark Nasila has been watching AI's ascent for over a decade, studying its effects on everything from agriculture and aviation to healthcare, education, entertainment, crime prevention, energy management, policy creation, finance, and anything in between, and applying them to his role at one of South Africa's most successful financial institutions, First National Bank, a division of FirstRand Group.
  African Artificial Intelligence is a comprehensive and fascinating journey, tracing the rise of AI and its evolution into the emerging technology underpinning all others – from connected devices and smart chatbots to the metaverse. Mark combines unexpected use cases and tales of cutting-edge innovation with a unique and potent argument: harnessing AI to solve Africa's problems requires embracing it from an African perspective.
  African nations can't afford to simply import AI solutions from afar. Instead, Mark contends, they need to rework, remix, and refine AI so it's able to meet uniquely African challenges in uniquely African ways, and to take advantage of the once-in-a-generation opportunity AI represents for every industry, sector, and person, everywhere.

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