Posts in tag

Google I/O


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, …

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Google I/O 2019 | Cloud TPU Pods: AI Supercomputing for Large Machine Learning Problems Cloud Tensor Processing Unit (TPU) is an ASIC designed by Google for neural network processing. TPUs feature a domain specific architecture designed specifically for accelerating TensorFlow training and prediction workloads and provides performance benefits on machine learning production use. Learn the …

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Google I/O 2019 | TF-Agents: A Flexible Reinforcement Learning Library for TensorFlow TF-Agents is a clean, modular, and well-tested open-source library for Deep Reinforcement Learning with TensorFlow. This session will cover recent advancements in Deep RL, and show how TF-Agents can help to jump start your project. You will also see how TF-Agent library components …

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Google I/O 2019 | Machine Learning Fairness: Lessons Learned ML fairness is a critical consideration in machine learning development. This session will present a few lessons Google has learned through our products and research and how developers can apply these learnings in their own efforts. Techniques and resources will be presented that enable evaluation and …

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Google I/O 2019 | Federated Learning: Machine Learning on Decentralized Data Meet federated learning: a technology for training and evaluating machine learning models across a fleet of devices (e.g. Android phones), orchestrated by a central server, without sensitive training data leaving any user’s device. Learn how this privacy-preserving technology is deployed in production in Google …

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Google I/O 2019 | Machine Learning Zero to Hero This is a talk for people who know code, but who don’t necessarily know machine learning. Learn the ‘new’ paradigm of machine learning, and how models are an alternative implementation for some logic scenarios, as opposed to writing if/then rules and other code. This session will …

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Google I/O 2019 | Swift for TensorFlow Swift for TensorFlow is a platform for the next generation of machine learning that leverages innovations like first-class differentiable programming to seamlessly integrate deep neural networks with traditional software development. In this session, learn how Swift for TensorFlow can make advanced machine learning research easier and why Jeremy …

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Google I/O 2019 | Train Custom Machine Learning Models with No Data Science Expertise Cloud AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their needs, by leveraging Google’s state-of-the-art neural architecture search technology. Learn the power and ease-of-use of Cloud AutoML …

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Google I/O 2019 | TensorFlow Extended: Machine Learning Pipelines and Model Understanding This talk will focus on creating a production machine learning pipeline using TFX. Using TFX developers can implement machine learning pipelines capable of processing large datasets for both modeling and inference. In addition to data wrangling and feature engineering over large datasets, TFX …

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Google I/O 2019 | Getting Started with TensorFlow 2.0 TensorFlow 2.0 is here! Understand new user-friendly APIs for beginners and experts through code examples to help you create different flavors of neural networks (Dense, Convolutional, and Recurrent) and understand when to use the Keras Sequential, Functional, and Subclassing APIs for your projects.   Speakers: Josh …

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