Google Releases Tensorflow 3D. What Exactly Is It?

Autonomous vehicles, robots, and other ML-learning systems have to case their surroundings to navigate and operate in the real world. They are often guided by 3D sensors such as Lidar, radar, and depth-sensing cameras and use scene understanding technology to process data these devices capture.

3D scene understanding is critical in object detection, human-centric understanding, and graphics. Though computer vision has made significant progress through mobile 3D object detection and transparent object detection, the number of tools that can be applied to 3D data is still limited.

To improve 3D scene understanding, Google has now developed TensorFlow 3D — a highly modular library to bring 3D deep learning capabilities to TensorFlow.

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Google's TPU's being primed for the Quantum Jump

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

As the world is gearing towards more automation and AI, the need for quantum computing has also grown exponentially. Quantum computing lies at the intersection of quantum physics and high-end computer technology, and in more than one way, hold the key to our AI-driven future.

Quantum computing requires state-of-the-art tools to perform high-end computing. This is where TPUs come in handy. TPUs or Tensor Processing Units are custom-built ASICs (Application Specific Integrated Circuits) to execute machine learning tasks efficiently. TPUs are specific hardware developed by Google for neural network machine learning, specially customised to Google’s Machine Learning software, Tensorflow.

The liquid-cooled Tensor Processing units, built to slot into server racks, can deliver up to 100 petaflops of compute. It powers Google products like Google Search, Gmail, Google Photos and Google Cloud AI APIs.

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Google Releases Tensorflow 3D. What Exactly Is It?

Autonomous vehicles, robots, and other ML-learning systems have to case their surroundings to navigate and operate in the real world. They are often guided by 3D sensors such as Lidar, radar, and depth-sensing cameras and use scene understanding technology to process data these devices capture.

3D scene understanding is critical in object detection, human-centric understanding, and graphics. Though computer vision has made significant progress through mobile 3D object detection and transparent object detection, the number of tools that can be applied to 3D data is still limited.

To improve 3D scene understanding, Google has now developed TensorFlow 3D — a highly modular library to bring 3D deep learning capabilities to TensorFlow.

#opinions #3d scene understanding tensorflow 3d google #tensorflow

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With the advancement in technology, many products have found a dire need to showcase their product virtually and to make the virtual experience as clear as actual a technology called 3D is used. The 3D technology allows a business to showcase their products in 3 dimensions virtually.

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What Are Google Compute Engine ? - Explained

What Are Google Compute Engine ? - Explained

The Google computer engine exchanges a large number of scalable virtual machines to serve as clusters used for that purpose. GCE can be managed through a RESTful API, command line interface, or web console. The computing engine is serviced for a minimum of 10-minutes per use. There is no up or front fee or time commitment. GCE competes with Amazon’s Elastic Compute Cloud (EC2) and Microsoft Azure.

https://www.mrdeluofficial.com/2020/08/what-are-google-compute-engine-explained.html

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Embedding your <image> in google colab <markdown>

This article is a quick guide to help you embed images in google colab markdown without mounting your google drive!

Just a quick intro to google colab

Google colab is a cloud service that offers FREE python notebook environments to developers and learners, along with FREE GPU and TPU. Users can write and execute Python code in the browser itself without any pre-configuration. It offers two types of cells: text and code. The ‘code’ cells act like code editor, coding and execution in done this block. The ‘text’ cells are used to embed textual description/explanation along with code, it is formatted using a simple markup language called ‘markdown’.

Embedding Images in markdown

If you are a regular colab user, like me, using markdown to add additional details to your code will be your habit too! While working on colab, I tried to embed images along with text in markdown, but it took me almost an hour to figure out the way to do it. So here is an easy guide that will help you.

STEP 1:

The first step is to get the image into your google drive. So upload all the images you want to embed in markdown in your google drive.

Step 2:

Google Drive gives you the option to share the image via a sharable link. Right-click your image and you will find an option to get a sharable link.

On selecting ‘Get shareable link’, Google will create and display sharable link for the particular image.

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