Why Google Colab? My warm welcome to all the readers!

1. Introduction:

Not everyone can indeed afford a powerful computing system. We have nothing but to adjust with less computing system which runs code for hours and even days. Most of us have got a system with RAM 4 GB or 8 GB and only a few have RAM beyond 8 GB with an advanced processor. What can we do if we have a less powerful computing system? Should we build-up patience and wait for the code to run for hours and even days? No, we don’t have to wait for hours or days to run certain codes. Here comes rescuer — Google Colab. Google Colab is also known as Google Colaboratory. Google Colab is a jupyter notebook environment. It is a free source provided by google wherein we can write and execute code. We can use Google Colab with ease just as we use local jupyter. Google Colab provides RAM of 12 GB with a maximum extension of 25 GB and a disk space of 358.27 GB. Wow! It is great to have a free source with such huge RAM and disk space.

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RAM and Disk Space provided by Google Colab

FYI: If certain code takes 1 hour to run in local Jupyter or Spyder or any other environment, same code takes around 10–15 minutes to run in Google Colab. Amazing, isn’t it!

2. How to Open and/or Upload Notebooks

Well, I must admit that it is not that tough to learn to use Google Colab. We can easily learn to use it in the first attempt. Click on the Google Colab link: https://colab.research.google.com/ which navigates us to the official site. This is how it looks when we click on the above link.

Image for post

Google Colab Official Site View

In the above image, we can see a dialog box with headings** ‘Examples’, ‘Recent’, ‘Google Drive’, ‘Github’, and ‘Upload’.** Each heading is nothing but sections through which we can open our notebook and use it for further analysis.

2.1 Examples:

Under this section, we can explore google colab like overview, guide, etc.

2.2 Recent:

Under this section, only those notebooks will be displayed which are recently used. You can directly go to the Recent section and open a recent notebook for further analysis.

2.3 Google Drive:

Under this section, all the notebooks which are in our google drive are displayed. We can access it by click on it and use for further analysis.

2.4 Github:

Under this section, all the notebooks which are in our Github are displayed. We just need to paste the respective link and get access to notebooks.

2.5 Upload

Under this section, we can upload notebooks from our local drive and access it.

If we want to create a new notebook, then we should click on Cancel button (Bottom Right) in the dialog box. Below is the image for reference.

#data-science #deep-learning #google-drive #machine-learning #google-colab #deep learning

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Buddha Community

Why Google Colab? My warm welcome to all the readers!
Jon  Gislason

Jon Gislason

1619247660

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.

#opinions #alphabet #asics #floq #google #google alphabet #google quantum computing #google tensorflow #google tensorflow quantum #google tpu #google tpus #machine learning #quantum computer #quantum computing #quantum computing programming #quantum leap #sandbox #secret development #tensorflow #tpu #tpus

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!

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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.

Image for post

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.

Image for post

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

#google-cloud-platform #google-collaboratory #google-colaboratory #google-cloud #google-colab #cloud

Why Google Colab? My warm welcome to all the readers!

1. Introduction:

Not everyone can indeed afford a powerful computing system. We have nothing but to adjust with less computing system which runs code for hours and even days. Most of us have got a system with RAM 4 GB or 8 GB and only a few have RAM beyond 8 GB with an advanced processor. What can we do if we have a less powerful computing system? Should we build-up patience and wait for the code to run for hours and even days? No, we don’t have to wait for hours or days to run certain codes. Here comes rescuer — Google Colab. Google Colab is also known as Google Colaboratory. Google Colab is a jupyter notebook environment. It is a free source provided by google wherein we can write and execute code. We can use Google Colab with ease just as we use local jupyter. Google Colab provides RAM of 12 GB with a maximum extension of 25 GB and a disk space of 358.27 GB. Wow! It is great to have a free source with such huge RAM and disk space.

Image for post

RAM and Disk Space provided by Google Colab

FYI: If certain code takes 1 hour to run in local Jupyter or Spyder or any other environment, same code takes around 10–15 minutes to run in Google Colab. Amazing, isn’t it!

2. How to Open and/or Upload Notebooks

Well, I must admit that it is not that tough to learn to use Google Colab. We can easily learn to use it in the first attempt. Click on the Google Colab link: https://colab.research.google.com/ which navigates us to the official site. This is how it looks when we click on the above link.

Image for post

Google Colab Official Site View

In the above image, we can see a dialog box with headings** ‘Examples’, ‘Recent’, ‘Google Drive’, ‘Github’, and ‘Upload’.** Each heading is nothing but sections through which we can open our notebook and use it for further analysis.

2.1 Examples:

Under this section, we can explore google colab like overview, guide, etc.

2.2 Recent:

Under this section, only those notebooks will be displayed which are recently used. You can directly go to the Recent section and open a recent notebook for further analysis.

2.3 Google Drive:

Under this section, all the notebooks which are in our google drive are displayed. We can access it by click on it and use for further analysis.

2.4 Github:

Under this section, all the notebooks which are in our Github are displayed. We just need to paste the respective link and get access to notebooks.

2.5 Upload

Under this section, we can upload notebooks from our local drive and access it.

If we want to create a new notebook, then we should click on Cancel button (Bottom Right) in the dialog box. Below is the image for reference.

#data-science #deep-learning #google-drive #machine-learning #google-colab #deep learning

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

#google compute engine #google compute engine tutorial #google app engine #google cloud console #google cloud storage #google compute engine documentation

Isaac Bailey

1614760670

Combine Google Sheets Workbooks (Files from Google Drive) to One Master Using Google Colab

Learn how to combine multiple Google Sheet workbooks (files) located in a folder on Google Drive to one master worksheet using Google Colab.

Subscribe: https://www.youtube.com/channel/UC8p19gUXJYTsUPEpusHgteQ

#google-colab #google-drive