AI News, Discussing TensorFlow History, Challenges, and Learning Perspective

Discussing TensorFlow History, Challenges, and Learning Perspective

The solution’s flexible architecture allows for deploying computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

This session with Yaroslav Bulatov and Lukasz Kaiser of the Google Brain team overviews the formation of TensorFlow in brief, provides some examples of the tool applied within Google products, plans for the future, etc.

(OpenAI is a non-profit artificial intelligence research organization founded by recognized machine learning/AI research engineers and scientists.) He highlighted the following aspects:

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Machine Learning APIs by Example (Google I/O '17)

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Computer Vision and Machine Learning, by Nick Wong

A basic introduction to some fundamental concepts in machine learning using Tensorflow, coupled with an introduction to OpenCV2, a computer vision project.

Heroes of Deep Learning: Andrew Ng interviews Andrej Karpathy

NVIDIA and Deep Learning Research with Bryan Catanzaro: GCPPodcast 119

Original post: Bryan Catanzaro the VP Applied Deep ..

Continuously Train & Deploy Spark ML and Tensorflow AI Models from Jupyter Notebook to Production

Notebook: ...

Cloud Machine Learning Engine with Yufeng Guo: GCPPodcast 71

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Lesson 2: Practical Deep Learning for Coders

CONVOLUTIONAL NEURAL NETWORKS For last week's assignment your goal was to get into the top 50% of the Kaggle Dogs v Cats competition. This week ...