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Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation;
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Use TensorFlow Enterprise with other GCP services to improve the speed and efficiency of machine learning pipelines for reliable and stable;
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compatible APIs across the TensorFlow ecosystem Learn and implement best practices for building data ingestion pipelines using TF 2.0 APIs Migrate;
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Tackle common commercial machine learning problems with Google's TensorFlow 1.x library and build deployable solutions. About This Book;
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training to model serving. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data;
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Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition;
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learning pipelines using the popular Tensorflow framework Train neural networks such as ConvNets, generative models, and LSTMs Includes projects;
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build high-efficiency data input pipelines Perform transfer learning and fine-tuning with TensorFlow Hub Define and train networks to solve;
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intelligence applications. Starting with an introduction to the fundamentals of deep reinforcement learning and TensorFlow 2.x, the book covers;
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Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. The book begins with;
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Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. The book begins with introducing;
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Explore machine learning concepts using the latest numerical computing library - TensorFlow - with the help of this comprehensive cookbook;
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of machine learning and neural network TensorFlow techniques. What you will learn Use tf.Keras for fast prototyping, building, and training deep;
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. The book begins with an example-driven approach to building input pipelines that feed all machine learning models. You will learn how to;
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, and you want to skip detailed theoretical explanations in favor of building production-ready machine learning models using TensorFlow, this;
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Skip the theory and get the most out of Tensorflow to build production-ready machine learning models Key Features Exploit the features;
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Learn how to develop intelligent applications with sequential learning and apply modern methods for language modeling with neural network;
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Master expert techniques for building automated and highly scalable end-to-end machine learning models and pipelines in Azure using;
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convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you;
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ML workflows Use code snippets as templates in building TensorFlow models and workflows Save development time by integrating prebuilt;
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Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices Key;
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This fully revised second edition of Machine Learning with TensorFlow teaches you the foundational concepts of machine learning and how to;
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set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later;
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machine learning algorithms. Machine Learning with TensorFlow teaches readers about machine learning algorithms and how to implement;
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TensorFlow on your computer Learn the fundamentals of statistical regression and neural networks Visualize the machine learning process with;
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with popular deep learning libraries, such as TensorFlow and PyTorch, to extend the capabilities of existing models. You'll also see how;
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learning models with TensorFlow and Keras. What you will learn Understand how deep learning, machine learning, and artificial;
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