DagsHub

JULY 25, 2024

TensorFlow The Google Brain team created the open-source deep learning framework TensorFlow, which was made available in 2015. Developed by François Chollet, it was released in 2015 to simplify the creation of deep learning models. Further Reading and Documentation H2O.ai Documentation H2O.ai

Deep Learning 52

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Towards AI

AUGUST 8, 2024

Developed by Google in 2015, TensorFlow boasts extensive capabilities, resulting in the tool being used often for research purposes or companies using it for their programming purposes. It can also be used in a variety of languages, such as Python, C++, JavaScript, and Java.

Deep Learning 91

Deep Learning Deep Learning Machine Learning Machine Learning 91

phData

MAY 25, 2023

Debuting in 2015, Power BI has undergone meaningful updates that have made it a leader not just in data visualization, but in the business intelligence space as well. In a perfect world, Microsoft would have clients push even more storage and compute to its Azure Synapse platform.

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Power BI Analytics Analytics Azure 84

IBM Journey to AI blog

NOVEMBER 13, 2023

In 2015, Google donated Kubernetes as a seed technology to the Cloud Native Computing Foundation (CNCF) (link resides outside ibm.com), the open-source, vendor-neutral hub of cloud-native computing. While Docker includes its own orchestration tool, called Docker Swarm , most developers choose Kubernetes container orchestration instead.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

Pickl AI

AUGUST 22, 2024

Overview of TensorFlow TensorFlow , developed by Google Brain, is a robust and versatile deep learning framework that was introduced in 2015. Launched in 2015, Keras was designed to simplify the process of building and experimenting with Deep Learning models.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

The MLOps Blog

NOVEMBER 30, 2022

Starting from AlexNet with 8 layers in 2012 to ResNet with 152 layers in 2015 – the deep neural networks have become deeper with time. Different tools: Your repository consists of multiple tools, libraries, and infrastructure providers like Azure, AWS, and GCP.

ML 52

ML ML Machine Learning Machine Learning 52