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Tag: Tensorflow

Simplify Data Conversion from Apache Spark to TensorFlow and PyTorch
Petastorm is a popular open-source library from Uber that enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. We are excited to announce that Petastorm 0.9.0 supports the easy conversion of data from Apache Spark DataFrame to TensorFlow Dataset and PyTorch …
Cloud Data Science 11
Even with the coronavirus causing mass closures, there are still some big announcements in the cloud data science world. Google is starting to take enterprise AI seriously and Amazon is … The post Cloud Data Science 11 appeared first on Data Science 101.
Book Review: Python Machine Learning – Third Edition by Sebastian Raschka, Vahid Mirjalili
I had been looking for a good book to recommend to my "Introduction to Data Science" classes at UCLA as a text to use once my class completes ... sort of the next step after learning the basics. That's why I was looking forward to reviewing the new 3rd edition …
Tensorflow basics : Matrix operations
4 Basic operations for working with matrixes in tensorflow
Databricks Demonstrates AWS Platform Integrations at re:Invent 2019
Databricks was proud to be a Platinum sponsor at re:Invent. The past year has been an exciting one for our partnership with AWS, as we built new integrations and deepened existing ones with so many AWS services. re:Invent was a great opportunity to showcase how our joint customers have benefitted …
Data Drift Detection for Image Classifiers
This article covers how to detect data drift for models that ingest image data as their input in order to prevent their silent degradation in production. Run the example in a complementary Domino project. Introduction: preventing silent model degradation in production In the real word, data is recorded by different …
Managed MLflow Now Available on Databricks Community Edition
In February 2016, we introduced Databricks Community Edition, a free edition for big data developers to learn and get started quickly with Apache Spark. Since then our commitment to foster a community of developers remains steadfast: to date, we have over 150K registered Community Edition users; we have trained thousands …
A Guide to Training Sessions at Spark + AI Summit, Europe
Education and the pursuit of knowledge are lifelong journeys: they never complete; there is always something new to learn; a new professional certification to add to your credit; a knowledge gap to fill. Training at Spark + AI Summit, Europe is not only about becoming an Apache Spark expert. Nor …
DarwinAI Generative Synthesis Platform and Intel Optimizations for TensorFlow Accelerate Neural Networks
DarwinAI, a Waterloo, Canada startup creating next-generation technologies for Artificial Intelligence development, announced that the company’s Generative Synthesis platform – when used with Intel technology and optimizations – generated neural networks with a 16.3X improvement in image classification inference performance. Intel shared the optimization results in a recently published solution …
How to Use MLflow To Reproduce Results and Retrain Saved Keras ML Models
In part 2 of our series on MLflow blogs, we demonstrated how to use MLflow to track experiment results for a Keras network model using binary classification. We classified reviews from an IMDB dataset as positive or negative. And we created one baseline model and two experiments. For each model, …
Identify Suspicious Behavior in Video with Databricks Runtime for Machine Learning
With the exponential growth of cameras and visual recordings, it is becoming increasingly important to operationalize and automate the process of video identification and categorization. Applications ranging from identifying the correct cat video to visually categorizing objects are becoming more prevalent. With millions of users around the world generating and …
Introducing mlflow-apps: A Repository of Sample Applications for MLflow
Introduction This summer, I was a software engineering intern at Databricks on the Machine Learning (ML) Platform team. As part of my intern project, I built a set of MLflow apps that demonstrate MLflow’s capabilities and offer the community examples to learn from. In this blog, I’ll discuss this library …
Bay Area Apache Spark Meetup Summary @ Databricks HQ
On July 19, we held our monthly Bay Area Spark Meetup (BASM) at Databricks, HQ in San Francisco. At the Spark + AI Summit in June, we announced two open-source projects: Project Hydrogen and MLflow. Partly to continue sharing the progress of these open-source projects with the community and partly …
A Guide to AI, Machine Learning, and Deep Learning Talks at Spark + AI Summit Europe
Within a couple of years of its release as an open-source machine learning and deep learning framework, TensorFlow has seen an amazing rate of adoption. Consider the number of stars on its github page: over 105K; look at the number of contributors: 1500+; and observe its growing penetration and pervasiveness …
Neural networks to communicate with Alexa devices using sign language
Many have found Amazon’s Alexa devices to be helpful in their homes, but…Tags: Alexa, neural network, sign language, TensorFlow
Neural networks to communicate with Alexa devices using sign language
Many have found Amazon’s Alexa devices to be helpful in their homes, but…Tags: Alexa, neural network, sign language, TensorFlow
Scalable End-to-End Deep Learning using TensorFlow™ and Databricks: On-Demand Webinar and FAQ Now Available!
On July 9th, our team hosted a live webinar—Scalable End-to-End Deep Learning using TensorFlow™ and Databricks—with Brooke Wenig, Data Science Solutions Consultant at Databricks and Sid Murching, Software Engineer at Databricks. In this webinar, we walked you through how to use TensorFlow™ and Horovod (an open-source library from Uber to …
How to Use MLflow, TensorFlow, and Keras with PyCharm
At Spark + AI Summit in June, we announced MLflow, an open-source platform for the complete machine learning cycle. The platform’s philosophy is simple: work with any popular machine learning library; allow machine learning developers experiment with their models, preserve the training environment, parameters, and dependencies, and reproduce their results; …