Shana  Towne

Shana Towne


Learn Why Agencies are Teaming Up with Netlify - Jamstack at Work

Partners benefit from working with Netlify in the following ways:
Generate new business opportunities: Agencies gain exposure and expertise in the growing Jamstack ecosystem by working alongside Netlify. Netlify is at the center of the modern web, and more than one million developers and businesses have joined Netlify’s platform so far. Netlify collaborates directly with client-facing teams to provide resources and support during new business meetings and client engagements.
Increase development velocity and gain efficiencies: Agencies can simplify web development and complete projects faster, while using tools and frameworks developers enjoy. Netlify removes the need to manage infrastructure for agencies and their clients, and agencies can manage multiple clients in one account.
Exceed client expectations: Agencies can set new standards for performance, uptime, and cost-savings in every project by embracing the Jamstack web architecture and adopting the latest development best practices. Simplify cloud infrastructure management for you and clients.
We offer two agency partnership models. A Netlify Agency Partnership is for agencies that manage the Netlify experience for their customers–whether large or small. A Global Alliance Partnership is for agencies who wish to introduce Netlify to their enterprise customers. All Agency Partners are eligible for lead sharing, a dedicated account manager or access to the partner team, co-selling support, promotional opportunities, early access to new features, access to the Partner Hub and Slack, technical workshops, webinars, and more. Agencies interested in working with the Jamstack should apply. To join the program or learn more, just reach out to the partner team.
Netlify recently announced an expansion to our plans. We now offer 3 team plans, ensuring your Netlify service can grow in capabilities as your business and client use-cases evolve. For agencies these plans make it easier for your developers to build better sites and work better with teammates in a more secure environment. We hope it lowers friction as agencies and their clients get started, and helps teams develop websites and apps even faster as you do more and more with the Jamstack. See our team plans for more information.
We have a few events in the coming weeks for agencies. Join the Jamstack for Agencies webinar to learn about Jamstack and the opportunities for your agency with Netlify. Attend the Headless Commerce Summit to hear case studies from agencies about how Jamstack is pushing e-commerce forward. The Jamstack Conf Virtual is an event where the Jamstack community comes together to celebrate the next generation of the web.

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Learn Why Agencies are Teaming Up with Netlify - Jamstack at Work
Chet  Lubowitz

Chet Lubowitz


How to Install Microsoft Teams on Ubuntu 20.04

Microsoft Teams is a communication platform used for Chat, Calling, Meetings, and Collaboration. Generally, it is used by companies and individuals working on projects. However, Microsoft Teams is available for macOS, Windows, and Linux operating systems available now.

In this tutorial, we will show you how to install Microsoft Teams on Ubuntu 20.04 machine. By default, Microsoft Teams package is not available in the Ubuntu default repository. However we will show you 2 methods to install Teams by downloading the Debian package from their official website, or by adding the Microsoft repository.

Install Microsoft Teams on Ubuntu 20.04

1./ Install Microsoft Teams using Debian installer file

01- First, navigate to teams app downloads page and grab the Debian binary installer. You can simply obtain the URL and pull the binary using wget;

$ wget${VERSION}_amd64.deb

#linux #ubuntu #install microsoft teams on ubuntu #install teams ubuntu #microsoft teams #teams #teams download ubuntu #teams install ubuntu #ubuntu install microsoft teams #uninstall teams ubuntu

Jerad  Bailey

Jerad Bailey


Google Reveals "What is being Transferred” in Transfer Learning

Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.

The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.

#developers corner #learn transfer learning #machine learning #transfer learning #transfer learning methods #transfer learning resources

Shana  Towne

Shana Towne


How Deep Learning Works with Different Neuron Layers

How Deep Learning Works with Different Neuron Layers
Artificial Intelligence, Machine Learning, and Deep Learning come under Data Science. These terms are small but have changed technology. They have given a new direction to technology. The first step to understanding how deep learning works is to grasp the differences between AI, ML, and Deep Learning

#deep learning working #how deep learning works #machine learning

sophia tondon

sophia tondon


5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

#machinelearningapps #machinelearningdevelopers #machinelearningexpert #machinelearningexperts #expertmachinelearningservices #topmachinelearningcompanies #machinelearningdevelopmentcompany

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Jackson  Crist

Jackson Crist


Intro to Reinforcement Learning: Temporal Difference Learning, SARSA Vs. Q-learning

Reinforcement learning (RL) is surely a rising field, with the huge influence from the performance of AlphaZero (the best chess engine as of now). RL is a subfield of machine learning that teaches agents to perform in an environment to maximize rewards overtime.

Among RL’s model-free methods is temporal difference (TD) learning, with SARSA and Q-learning (QL) being two of the most used algorithms. I chose to explore SARSA and QL to highlight a subtle difference between on-policy learning and off-learning, which we will discuss later in the post.

This post assumes you have basic knowledge of the agent, environment, action, and rewards within RL’s scope. A brief introduction can be found here.

The outline of this post include:

  • Temporal difference learning (TD learning)
  • Parameters
  • QL & SARSA
  • Comparison
  • Implementation
  • Conclusion

We will compare these two algorithms via the CartPole game implementation. This post’s code can be found here :QL code ,SARSA code , and the fully functioning code . (the fully-functioning code has both algorithms implemented and trained on cart pole game)

The TD learning will be a bit mathematical, but feel free to skim through and jump directly to QL and SARSA.

#reinforcement-learning #artificial-intelligence #machine-learning #deep-learning #learning