Utvidet returrett til 31. januar 2025

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  • av Thomas V. Joseph
    485,-

    Architecting AI: Design patterns for building deep learning productsKEY FEATURES ¿ Master foundational concepts in design patterns of deep learning.¿ Benefit from practical insights shared by an industry professional.¿ Learn to build data products using deep learning.DESCRIPTION Design Patterns of Deep Learning with TensorFlow is your comprehensive guide to learning deep learning from a design pattern perspective. In this book, we explore deep learning within the context of building hyper-personalization models, exploring its applications across various industries and scenarios. It starts by showing how deep learning enhances retail through customer segmentation and data analysis. You will learn neural networks, computer vision with CNNs, and NLP for analyzing customer behavior. This book addresses challenges like uneven data and optimizing models with techniques like backpropagation, hyperparameter tuning, and transfer learning. Finally, it covers setting up data pipelines and deploying your system. With practical tips and actionable advice, this book equips readers with the skills and strategies needed to thrive in today's competitive AI landscape.By the end of this book, you will be equipped with the knowledge and practical skills to build and deploy deep learning-powered hyper-personalization systems that deliver exceptional customer experiences.WHAT YOU WILL LEARN¿ Understand about hyper-personalized AI models for tailored user experiences.¿ Design principles of computer vision and NLP models.¿ Inner working of transformers equipping readers to understand the intricacies of generative AI and large language models (LLMs) like ChatGPT.¿ To get the best out of deep learning models through hyperparameter tuning and transfer learning.¿ Learn how to build deployment pipelines to serve models into production environments seamlessly.WHO THIS BOOK IS FORThis book caters to both beginners and experienced practitioners in the field of data science and Machine Learning. Through practical examples, it simplifies complex ideas, linking them to design patterns.

  • av Thomas V. Joseph, Blaine Bateman, William So & m.fl.
    569,-

  • - Learn how you can build machine learning models and create your own real-world data science projects, 2nd Edition
    av Andrew Worsley, Anthony So, Thomas V. Joseph, m.fl.
    518,-

    The Data Science Workshop equips you with the basic skills you need to start working on a variety of data science projects. You'll work through the essential building blocks of a data science project gradually through the book, and then put all the pieces together to consolidate your knowledge and apply your learnings in the real world.

  • av Anthony So, Thomas V. Joseph, Mirza Rahim Baig, m.fl.
    519,-

    Take a hands-on approach to understanding deep learning and build smart applications that can recognize images and interpret textKey Features Understand how to implement deep learning with TensorFlow and Keras Learn the fundamentals of computer vision and image recognition Study the architecture of different neural networksBook DescriptionAre you fascinated by how deep learning powers intelligent applications such as self-driving cars, virtual assistants, facial recognition devices, and chatbots to process data and solve complex problems? Whether you are familiar with machine learning or are new to this domain, The Deep Learning Workshop will make it easy for you to understand deep learning with the help of interesting examples and exercises throughout.The book starts by highlighting the relationship between deep learning, machine learning, and artificial intelligence and helps you get comfortable with the TensorFlow 2.0 programming structure using hands-on exercises. You'll understand neural networks, the structure of a perceptron, and how to use TensorFlow to create and train models. The book will then let you explore the fundamentals of computer vision by performing image recognition exercises with convolutional neural networks (CNNs) using Keras. As you advance, you'll be able to make your model more powerful by implementing text embedding and sequencing the data using popular deep learning solutions. Finally, you'll get to grips with bidirectional recurrent neural networks (RNNs) and build generative adversarial networks (GANs) for image synthesis.By the end of this deep learning book, you'll have learned the skills essential for building deep learning models with TensorFlow and Keras.What you will learn Understand how deep learning, machine learning, and artificial intelligence are different Develop multilayer deep neural networks with TensorFlow Implement deep neural networks for multiclass classification using Keras Train CNN models for image recognition Handle sequence data and use it in conjunction with RNNs Build a GAN to generate high-quality synthesized imagesWho this book is forIf you are interested in machine learning and want to create and train deep learning models using TensorFlow and Keras, this workshop is for you. A solid understanding of Python and its packages, along with basic machine learning concepts, will help you to learn the topics quickly.

  • - A New, Interactive Approach to Learning Data Science
    av Andrew Worsley, Anthony So, Thomas V. Joseph, m.fl.
    518,-

    Cut through the noise and get real results with a step-by-step approach to data science

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