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  1. TensorFlow

    An end-to-end open source machine learning platform for everyone. Discover TensorFlow's flexible ecosystem of tools, libraries and community resources.

  2. Machine learning education | TensorFlow

    In this four-course Specialization taught by a TensorFlow developer, you'll explore the tools and software developers use to build scalable AI-powered algorithms in TensorFlow.

  3. Tutorials | TensorFlow Core

    Sep 19, 2023 · Complete, end-to-end examples to learn how to use TensorFlow for ML beginners and experts. Try tutorials in Google Colab - no setup required.

  4. Introduction to TensorFlow

    Using production-level tools to automate and track model training over the lifetime of a product, service, or business process is critical to success. TFX provides software frameworks and …

  5. Magenta

    A research project exploring the role of machine learning in the process of creating art and music.

  6. MIT Introduction to Deep Learning — The TensorFlow Blog

    Feb 28, 2019 · We’ve designed three open-source, interactive TensorFlow software labs that cover the basics of TensorFlow, recurrent neural network models for music generation, …

  7. A Neural Network Playground

    You’re free to use it in any way that follows our Apache License. And if you have any suggestions for additions or changes, please let us know. We’ve also provided some controls below to …

  8. How TensorFlow helps Edge Impulse make ML accessible to …

    Jun 2, 2021 · The SDK has a permissive open source license, so developers are free to use it in any project or share it with others. There are two main options for running deep learning …

  9. TFX Airflow 教程 - TensorFlow

    sudo apt-get install \ build-essential libssl-dev libffi-dev \ libxml2-dev libxslt1-dev zlib1g-dev \ python3-pip git software-properties-common 如果您运行的是 Python 3.6,应该安装 python3.6 …

  10. tf.compat.v1.losses.softmax_cross_entropy | TensorFlow v2.16.1

    tf.compat.v1.losses.softmax_cross_entropy( onehot_labels, logits, weights=1.0, label_smoothing=0, scope=None, loss_collection=ops.GraphKeys.LOSSES, …