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Practical AI and Computer Vision Projects with Python, Keras, and TensorFlow

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Книга предлагает практические примеры и проекты по глубокому обучению с использованием Python, Keras и TensorFlow, охватывая темы от классификации изображений до создания приложений для мобильных и облачных систем. Она предназначена для разработчиков, специалистов по данным и энтузиастов, стремящихся освоить современные технологии ИИ.
  • main points
  • unique insights
  • practical applications
  • key topics
  • key insights
  • learning outcomes

  • • main points

    • 1
      Широкий охват тем, связанных с глубоким обучением и компьютерным зрением.
    • 2
      Практические примеры и проекты, которые помогают читателям применять теорию на практике.
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      Подробные инструкции по использованию популярных фреймворков, таких как Keras и TensorFlow.

  • • unique insights

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      Интеграция облачных API для компьютерного зрения в реальных проектах.
    • 2
      Использование переноса обучения для повышения эффективности моделей.

  • • practical applications

    • Книга предоставляет читателям практические навыки и инструменты для разработки приложений ИИ, что делает ее ценным ресурсом для профессионалов и новичков.

  • • key topics

    • 1
      Глубокое обучение
    • 2
      Компьютерное зрение
    • 3
      Применение Keras и TensorFlow

  • • key insights

    • 1
      Практические проекты, которые можно реализовать в реальных условиях.
    • 2
      Подход к обучению, который сочетает теорию и практику.
    • 3
      Обширное покрытие современных инструментов и технологий в области ИИ.

  • • learning outcomes

    • 1
      Understand the fundamentals of deep learning and computer vision.
    • 2
      Gain practical skills in using Keras and TensorFlow for real-world applications.
    • 3
      Learn to implement advanced techniques such as transfer learning and cloud-based APIs.
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“ Introduction to Artificial Intelligence and Computer Vision

Artificial Intelligence (AI) and Computer Vision (CV) are rapidly transforming various industries, offering innovative solutions to complex problems. This article explores the landscape of AI and CV, highlighting practical applications and key concepts.

“ Key Concepts: Deep Learning, Python, Keras, and TensorFlow

Deep Learning, a subset of AI, has gained prominence due to its ability to handle large datasets and complex patterns. Python, along with libraries like Keras and TensorFlow, provides a powerful toolkit for developing and deploying deep learning models. These tools enable developers to create sophisticated AI applications with relative ease.

“ Practical Applications of Deep Learning

Deep learning is used in a wide range of applications, including image recognition, natural language processing, and autonomous vehicles. The ability to train models on vast amounts of data has led to significant advancements in these fields. This article will delve into practical examples of how deep learning can be applied to solve real-world problems.

“ Image Classification with Keras

Image classification is a fundamental task in computer vision. Keras simplifies the process of building and training image classification models. Using Keras, developers can quickly prototype and deploy models that can accurately classify images into different categories. This section will provide a step-by-step guide on how to build an image classification model using Keras.

“ Transfer Learning: Cats vs. Dogs Example

Transfer learning is a technique that allows developers to leverage pre-trained models to solve new problems. The 'Cats vs. Dogs' example is a classic demonstration of transfer learning, where a model trained on a large dataset is fine-tuned to classify images of cats and dogs. This approach significantly reduces the amount of data and training time required to achieve high accuracy.

“ Building AI Applications for Cloud, Mobile, and Edge Systems

AI applications can be deployed on various platforms, including cloud, mobile, and edge systems. Each platform has its own unique challenges and opportunities. Cloud-based AI applications offer scalability and accessibility, while mobile and edge-based applications provide real-time processing and privacy benefits. This section will explore the considerations for building AI applications on different platforms.

“ Responsible AI: Bias, Transparency, and Reproducibility

As AI becomes more prevalent, it is crucial to address ethical concerns such as bias, transparency, and reproducibility. Biased datasets can lead to unfair or discriminatory outcomes. Transparency and explainability are essential for understanding how AI models make decisions. Reproducibility ensures that AI models can be reliably replicated and validated. This section will discuss the importance of responsible AI and provide strategies for mitigating ethical risks.

“ Conclusion: The Future of AI and Computer Vision

AI and computer vision are poised to revolutionize various industries, from healthcare to transportation. As technology continues to evolve, it is essential to address ethical concerns and ensure that AI is used for the benefit of society. The future of AI and computer vision is bright, with endless possibilities for innovation and progress.

 Original link: https://mgsu.ru/postupayushchim/olimp/olimpiady/stroitelnaya-olimpiada-imeni-n-s-streletskogo-2022/iskusstvennyy-intellekt-v-stroitelstve/Iskusstvenny_intellekt_i_kompyuternoe_zrenie.pdf

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