TensorFlow

Can I use both TensorFlow and Tesseract OCR to create a model that detects text out of a document pdf

Need your kind suggestion if I should choose TensorFlow.js or TensorFlow with Python for ML models. As I don't want to go and have not gone too deep in JavaScript, I need your suggestion.

I am working on a data collection project which requires processing images of playing cards for a deck-building game called StarRealms (https://www.starrealms.com/). I need to be able to read in a database of images or videos and recognize each card as a game is played. I believe this would involve some computer vision for image recognition and TensorFlow modeling to ensure an accurate prediction. Which technology stack would be the best for this job? Thank you for the help!

I am going to send my website to a Venture Capitalist for inspection. If I succeed, I will get funding for my StartUp! This website is based on Django and Uses Keras and TensorFlow model to predict medical imaging. Should I use Heroku or PythonAnywhere to deploy my website ?? Best Regards, Adarsh.

Hello All, I have concerns about which framework to use in my case. I'm working on a project that uses TensorFlow for implementing CNN and image processing, it also deals with a huge dataset. Shall I implement the rest APIs in Phalcon because of its speed and great performance or Falcon since I'm working with TensorFlow and doing image processing steps?

PS: APIs are to receive the image from the user, and call *.py files to execute image processing steps and CNN Thanks In Advance :D

Google Analytics is a great tool to analyze your traffic. To debug our software and ask questions, we love to use Postman and Stack Overflow. Google Drive helps our team to share documents. We're able to build our great products through the APIs by Google Maps, CloudFlare, Stripe, PayPal, Twilio, Let's Encrypt, and TensorFlow.

Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

https://eng.uber.com/horovod/

(Direct GitHub repo: https://github.com/uber/horovod)

In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.

Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

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Deep learning jobs require a unique challenge versus other jobs that run across multiple GPUs: they need every node to stay up and running till the job is complete, which is why Uber uses gang scheduling.

Gang scheduling (an optimization algorithm) means that for a cluster computing job to run, all the nodes have to be ready to run at the same time. This is especially useful in deep learning training, which involves constant feedback exchanged between nodes. Uber implemented gang scheduling in an Open Source framework called Horovod, to run Google’s TensorFlow machine learning software across multiple nodes.

Because they needed GPUs in upstream releases as well, Uber’s engineers chose to use Mesos containers over Docker.

The engineers at Uber used Horovod (and the TensorFlow package compatible with it) because it was easier to learn the rules of the MPI library in Horovod, than learning an entirely new system.