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Keras

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TF Dev Summit ’19 | Introducing TensorFlow 2.0 and its High-Level APIs

TF Dev Summit ’19 | Introducing TensorFlow 2.0 and its high-level APIs At TensorFlow Dev Summit 2019, the TensorFlow team introduced the Alpha version of TensorFlow 2.0! With TensorFlow 2.0, we are consolidating our APIs and integrating Keras across the TensorFlow ecosystem. In this talk, we give an overview of what to expect with TensorFlow High Level APIs in 2.0. See the revamped dev site → https://www.tensorflow.org/
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  • Data
  • Programming

TF Dev Summit ’19 | TensorFlow Probability: Learning With Confidence

TF Dev Summit ’19 | TensorFlow Probability: Learning with confidence TensorFlow Probability (TFP) is a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware (TPU, GPU). It’s for data scientists, statisticians, and ML researchers/practitioners who want to encode domain knowledge to understand data and make predictions with uncertainty estimates. In this talk we focus on the “layers” module and demonstrate how TFP “distributions” fit naturally with Keras to enable estimating aleatoric and/or epistemic uncertainty. Speaker: Josh Dillon, Software Engineer
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How To Install Keras

Overview This guide shows how to install Keras. Keras is a high-level neural networks API written and for Python. It is also capable of running on top of TensorFlow.   Prerequisites Python has been installed Installation on Ubuntu Installation on Windows Optional but recommended. Setup a VirtualEnvironment and Pip has been installed. VirtualEnvironment for Ubuntu TensorFlow has been installed   Installation 01. Activate your virtual environment, or skip this step if not using virtual environment.   02. Install the Keras package   03. Verify that Keras has been installed by viewing the version.  
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  • Artificial Intelligence
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Google I/O 2019 | Cutting Edge TensorFlow: New Techniques

Google I/O 2019 | Cutting Edge TensorFlow: New Techniques There’s lots of great new things available in TensorFlow since last year’s IO. This session will take you through 4 of the hottest from Hyperparameter Tuning with Keras Tuner to Probabilistic Programming to being able to rank your data with learned ranking techniques and TF-Ranking. Finally, you will look at TF-Graphics that brings 3D functionalities to TensorFlow.   Speakers: Elie Burzstein , Josh Dillon, Michael Bendersky, Sofien Bouaziz Session ID: TDA482
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  • Machine Learning

PyCon 2019 | Getting Started With Deep Learning: Using Keras & Numpy To Detect Voice Disorders

PyCon 2019 | Getting Started With Deep Learning: Using Keras & Numpy To Detect Voice Disorders Speaker: Deborah Hanus, Sebastian Hanus   Deep learning is a useful tool for problems in computer vision, natural language processing, and medicine. While it might seem difficult to get started in deep learning, Python libraries, such as Keras make deep learning quite accessible. In this talk, we will discuss what deep learning is, introduce NumPy and Keras, and discuss common mistakes and debugging strategies. Throughout the talk, we will return to an example project in the medical domain, which used deep learning on vocal…
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  • Machine Learning
  • Platforms

Google I/O 2019 | Live Coding A Machine Learning Model from Scratch

Google I/O 2019 | Live Coding A Machine Learning Model from Scratch Do you want to build a machine learning model, but not sure where to start? In this session, learn how to start with an empty Colab notebook, code a model using TensorFlow and Keras, train the model live, deploy it to Cloud AI Platform for serving, and use the deployed model to generate predictions from a web app. Speaker: Sara Robinson Session ID: T81AAF Citi I/O Aster.Cloud DotLAH!
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